@article{DBLP:journals/corr/abs-1803-02735,
  author       = {Muhammad Haris and
                  Greg Shakhnarovich and
                  Norimichi Ukita},
  title        = {Deep Back-Projection Networks For Super-Resolution},
  journal      = {CoRR},
  volume       = {abs/1803.02735},
  year         = {2018},
  url          = {http://arxiv.org/abs/1803.02735},
  eprinttype    = {arXiv},
  eprint       = {1803.02735},
  timestamp    = {Tue, 28 Jun 2022 16:27:02 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1803-02735.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1803-08664,
  author       = {Namhyuk Ahn and
                  Byungkon Kang and
                  Kyung{-}Ah Sohn},
  title        = {Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual
                  Network},
  journal      = {CoRR},
  volume       = {abs/1803.08664},
  year         = {2018},
  url          = {http://arxiv.org/abs/1803.08664},
  eprinttype    = {arXiv},
  eprint       = {1803.08664},
  timestamp    = {Mon, 13 Aug 2018 16:49:11 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1803-08664.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/corr/abs-1807-02758,
  author       = {Yulun Zhang and
                  Kunpeng Li and
                  Kai Li and
                  Lichen Wang and
                  Bineng Zhong and
                  Yun Fu},
  title        = {Image Super-Resolution Using Very Deep Residual Channel Attention
                  Networks},
  journal      = {CoRR},
  volume       = {abs/1807.02758},
  year         = {2018},
  url          = {http://arxiv.org/abs/1807.02758},
  eprinttype    = {arXiv},
  eprint       = {1807.02758},
  timestamp    = {Fri, 25 Oct 2024 10:06:04 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1807-02758.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1809-00219,
  author       = {Xintao Wang and
                  Ke Yu and
                  Shixiang Wu and
                  Jinjin Gu and
                  Yihao Liu and
                  Chao Dong and
                  Chen Change Loy and
                  Yu Qiao and
                  Xiaoou Tang},
  title        = {{ESRGAN:} Enhanced Super-Resolution Generative Adversarial Networks},
  journal      = {CoRR},
  volume       = {abs/1809.00219},
  year         = {2018},
  url          = {http://arxiv.org/abs/1809.00219},
  eprinttype    = {arXiv},
  eprint       = {1809.00219},
  timestamp    = {Tue, 17 Sep 2024 18:06:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1809-00219.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{liang2021swinirimagerestorationusing,
      title={SwinIR: Image Restoration Using Swin Transformer}, 
      author={Jingyun Liang and Jiezhang Cao and Guolei Sun and Kai Zhang and Luc Van Gool and Radu Timofte},
      year={2021},
      eprint={2108.10257},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2108.10257}, 
}

@article{DBLP:journals/corr/ShiCHTABRW16,
  author       = {Wenzhe Shi and
                  Jose Caballero and
                  Ferenc Husz{\'{a}}r and
                  Johannes Totz and
                  Andrew P. Aitken and
                  Rob Bishop and
                  Daniel Rueckert and
                  Zehan Wang},
  title        = {Real-Time Single Image and Video Super-Resolution Using an Efficient
                  Sub-Pixel Convolutional Neural Network},
  journal      = {CoRR},
  volume       = {abs/1609.05158},
  year         = {2016},
  url          = {http://arxiv.org/abs/1609.05158},
  eprinttype    = {arXiv},
  eprint       = {1609.05158},
  timestamp    = {Mon, 13 Aug 2018 16:47:09 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/ShiCHTABRW16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1708-02209,
  author       = {Ying Tai and
                  Jian Yang and
                  Xiaoming Liu and
                  Chunyan Xu},
  title        = {MemNet: {A} Persistent Memory Network for Image Restoration},
  journal      = {CoRR},
  volume       = {abs/1708.02209},
  year         = {2017},
  url          = {http://arxiv.org/abs/1708.02209},
  eprinttype    = {arXiv},
  eprint       = {1708.02209},
  timestamp    = {Mon, 13 Aug 2018 16:48:09 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1708-02209.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{Hayit_SR_MR,
    author = {Greenspan, Hayit},
    title = "{Super-Resolution in Medical Imaging}",
    journal = {The Computer Journal},
    volume = {52},
    number = {1},
    pages = {43-63},
    year = {2008},
    month = {02},
    abstract = "{This paper provides an overview on super-resolution (SR) research in medical imaging applications. Many imaging modalities exist. Some provide anatomical information and reveal information about the structure of the human body, and others provide functional information, locations of activity for specific activities and specified tasks. Each imaging system has a characteristic resolution, which is determined based on physical constraints of the system detectors that are in turn tuned to signal-to-noise and timing considerations. A common goal across systems is to increase the resolution, and as much as possible achieve true isotropic 3-D imaging. SR technology can serve to advance this goal. Research on SR in key medical imaging modalities, including MRI, fMRI and PET, has started to emerge in recent years and is reviewed herein. The algorithms used are mostly based on standard SR algorithms. Results demonstrate the potential in introducing SR techniques into practical medical applications.}",
    issn = {0010-4620},
    doi = {10.1093/comjnl/bxm075},
    url = {https://doi.org/10.1093/comjnl/bxm075},
    eprint = {https://academic.oup.com/comjnl/article-pdf/52/1/43/1153004/bxm075.pdf},
}
@INPROCEEDINGS{Isaac_SR,
  author={Isaac, Jithin Saji and Kulkarni, Ramesh},
  booktitle={2015 International Conference on Technologies for Sustainable Development (ICTSD)}, 
  title={Super resolution techniques for medical image processing}, 
  year={2015},
  volume={},
  number={},
  pages={1-6},
  keywords={Computed tomography;Medical diagnostic imaging;Noise;Magnetic resonance imaging;Spatial resolution;super resolution;medical images;MRI;CT},
  doi={10.1109/ICTSD.2015.7095900}}
@inproceedings{Sano_SR,
author = {Sano, Yoshiki and Mori, Takuma and Goto, Tomio and Hirano, Satoshi and Funahashi, Koji},
year = {2017},
month = {10},
pages = {1-2},
title = {Super-resolution method and its application to medical image processing},
doi = {10.1109/GCCE.2017.8229301}
}
@article{Gu_SR,
author = {Gu, Yuchong and Zeng, Zitao and Chen, Haibin and Wei, Jun and Zhang, Yaqin and Chen, Binghui and Li, Yingqin and Qin, Yujuan and Xie, Qing and Jiang, Zhuoren and lu, Yao},
year = {2020},
month = {08},
pages = {},
title = {MedSRGAN: medical images super-resolution using generative adversarial networks},
volume = {79},
journal = {Multimedia Tools and Applications},
doi = {10.1007/s11042-020-08980-w}
}

@article{accelaratedMRI,
author = {Block, Kai Tobias and Uecker, Martin and Frahm, Jens},
title = {Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint},
journal = {Magnetic Resonance in Medicine},
volume = {57},
number = {6},
pages = {1086-1098},
keywords = {compressed sensing, inverse problems, iterative reconstruction, projection reconstruction, regridding},
doi = {https://doi.org/10.1002/mrm.21236},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.21236},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.21236},
abstract = {Abstract The reconstruction of artifact-free images from radially encoded MRI acquisitions poses a difficult task for undersampled data sets, that is for a much lower number of spokes in k-space than data samples per spoke. Here, we developed an iterative reconstruction method for undersampled radial MRI which (i) is based on a nonlinear optimization, (ii) allows for the incorporation of prior knowledge with use of penalty functions, and (iii) deals with data from multiple coils. The procedure arises as a two-step mechanism which first estimates the coil profiles and then renders a final image that complies with the actual observations. Prior knowledge is introduced by penalizing edges in coil profiles and by a total variation constraint for the final image. The latter condition leads to an effective suppression of undersampling (streaking) artifacts and further adds a certain degree of denoising. Apart from simulations, experimental results for a radial spin-echo MRI sequence are presented for phantoms and human brain in vivo at 2.9 T using 24, 48, and 96 spokes with 256 data samples. In comparison to conventional reconstructions (regridding) the proposed method yielded visually improved image quality in all cases. Magn Reson Med 57:1086–1098, 2007. © 2007 Wiley-Liss, Inc.},
year = {2007}
}
@article{Spiral,
title = {Spiral imaging in fMRI},
journal = {NeuroImage},
volume = {62},
number = {2},
pages = {706-712},
year = {2012},
note = {20 YEARS OF fMRI},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2011.10.039},
url = {https://www.sciencedirect.com/science/article/pii/S1053811911012031},
author = {Gary H. Glover},
keywords = {Functional magnetic resonance imaging (fMRI), Spiral},
abstract = {T2*-weighted Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) requires efficient acquisition methods in order to fully sample the brain in a several second time period. The most widely used approach is Echo Planar Imaging (EPI), which utilizes a Cartesian trajectory to cover k-space. This trajectory is subject to ghosts from off-resonance and gradient imperfections and is intrinsically sensitive to cardiac-induced pulsatile motion from substantial first- and higher order moments of the gradient waveform near the k-space origin. In addition, only the readout direction gradient contributes significant energy to the trajectory. By contrast, the spiral method samples k-space with an Archimedean or similar trajectory that begins at the k-space center and spirals to the edge (spiral-out), or its reverse, ending at the origin (spiral-in). Spiral methods have reduced sensitivity to motion, shorter readout times, improved signal recovery in most frontal and parietal brain regions, and exhibit blurring artifacts instead of ghosts or geometric distortion. Methods combining spiral-in and spiral-out trajectories have further advantages in terms of diminished susceptibility-induced signal dropout and increased BOLD signal. In measurements of temporal signal to noise ratio measured in 8 subjects, spiral-in/out exhibited significant increases over EPI in voxel volumes recovered in frontal and whole brain regions (18% and 10%, respectively).}
}
@INPROCEEDINGS{Mamata,
  author={Shrestha, Mamata and Wang, Nian and Nakarmi, Ukash},
  booktitle={2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)}, 
  title={On Training Model Bias of Deep Learning based Super-resolution Frameworks for Magnetic Resonance Imaging}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Degradation;Training;Deep learning;Visualization;Magnetic resonance imaging;Biological system modeling;Superresolution;MRI;Super-resolution;Model Bias;Diverse Degradation model},
  doi={10.1109/BHI58575.2023.10313380}}

@article{Score-based,
  author       = {Yang Song and
                  Jascha Sohl{-}Dickstein and
                  Diederik P. Kingma and
                  Abhishek Kumar and
                  Stefano Ermon and
                  Ben Poole},
  title        = {Score-Based Generative Modeling through Stochastic Differential Equations},
  journal      = {CoRR},
  volume       = {abs/2011.13456},
  year         = {2020},
  url          = {https://arxiv.org/abs/2011.13456},
  eprinttype    = {arXiv},
  eprint       = {2011.13456},
  timestamp    = {Wed, 02 Dec 2020 10:10:16 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2011-13456.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{ddpm,
  author       = {Jonathan Ho and
                  Ajay Jain and
                  Pieter Abbeel},
  title        = {Denoising Diffusion Probabilistic Models},
  journal      = {CoRR},
  volume       = {abs/2006.11239},
  year         = {2020},
  url          = {https://arxiv.org/abs/2006.11239},
  eprinttype    = {arXiv},
  eprint       = {2006.11239},
  timestamp    = {Tue, 23 Jun 2020 17:57:22 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2006-11239.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{beatsGan,
  author       = {Prafulla Dhariwal and
                  Alex Nichol},
  title        = {Diffusion Models Beat GANs on Image Synthesis},
  journal      = {CoRR},
  volume       = {abs/2105.05233},
  year         = {2021},
  url          = {https://arxiv.org/abs/2105.05233},
  eprinttype    = {arXiv},
  eprint       = {2105.05233},
  timestamp    = {Fri, 14 May 2021 12:13:30 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2105-05233.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@misc{DPS,
      title={Diffusion Posterior Sampling for General Noisy Inverse Problems}, 
      author={Hyungjin Chung and Jeongsol Kim and Michael T. Mccann and Marc L. Klasky and Jong Chul Ye},
      year={2023},
      eprint={2209.14687},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
@misc{AcceleratedMRI,
      title={Score-based diffusion models for accelerated MRI}, 
      author={Hyungjin Chung and Jong Chul Ye},
      year={2022},
      eprint={2110.05243},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
@misc{song2022solving,
      title={Solving Inverse Problems in Medical Imaging with Score-Based Generative Models}, 
      author={Yang Song and Liyue Shen and Lei Xing and Stefano Ermon},
      year={2022},
      eprint={2111.08005},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
@article{ConditioningDDPM,
  author       = {Jooyoung Choi and
                  Sungwon Kim and
                  Yonghyun Jeong and
                  Youngjune Gwon and
                  Sungroh Yoon},
  title        = {{ILVR:} Conditioning Method for Denoising Diffusion Probabilistic
                  Models},
  journal      = {CoRR},
  volume       = {abs/2108.02938},
  year         = {2021},
  url          = {https://arxiv.org/abs/2108.02938},
  eprinttype    = {arXiv},
  eprint       = {2108.02938},
  timestamp    = {Mon, 24 Apr 2023 14:58:38 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2108-02938.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@Inbook{Robbins1992,
author="Robbins, Herbert E.",
editor="Kotz, Samuel
and Johnson, Norman L.",
title="An Empirical Bayes Approach to Statistics",
bookTitle="Breakthroughs in Statistics: Foundations and Basic Theory",
year="1992",
publisher="Springer New York",
address="New York, NY",
pages="388--394",
abstract="Let X be a random variable which for simplicity we shall assume to have discrete values x and which has a probability distribution depending in a known way on an unknown real parameter A,(1){\$}{\$}p{\backslash}left( {\{}x|{\backslash}lambda {\}} {\backslash}right) = Pr[X = x|{\backslash}Lambda  = {\backslash}lambda ],{\$}{\$}A itself being a random variable with a priori distribution function(2){\$}{\$}G{\backslash}left( {\backslash}lambda  {\backslash}right) = {\backslash}operatorname{\{}P{\}} r[{\backslash}Lambda {\{}{\backslash}text{\{} {\}}{\}}{\backslash}underline  {\backslash}leqslant  {\{}{\backslash}text{\{} {\}}{\}}{\backslash}lambda ].{\$}{\$}",
isbn="978-1-4612-0919-5",
doi="10.1007/978-1-4612-0919-5_26",
url="https://doi.org/10.1007/978-1-4612-0919-5_26"
}
@misc{chung2022improving,
      title={Improving Diffusion Models for Inverse Problems using Manifold Constraints}, 
      author={Hyungjin Chung and Byeongsu Sim and Dohoon Ryu and Jong Chul Ye},
      year={2022},
      eprint={2206.00941},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{ANDERSON1982313,
title = {Reverse-time diffusion equation models},
journal = {Stochastic Processes and their Applications},
volume = {12},
number = {3},
pages = {313-326},
year = {1982},
issn = {0304-4149},
doi = {https://doi.org/10.1016/0304-4149(82)90051-5},
url = {https://www.sciencedirect.com/science/article/pii/0304414982900515},
author = {Brian D.O. Anderson},
abstract = {Reverse-time stochastic diffusion equation models are defined and it is shown how most processes defined via a forward-time or conventional diffusion equation model have an associated reverse-time model.}
}
@article{Song2019,
  author       = {Yang Song and
                  Stefano Ermon},
  title        = {Generative Modeling by Estimating Gradients of the Data Distribution},
  journal      = {CoRR},
  volume       = {abs/1907.05600},
  year         = {2019},
  url          = {http://arxiv.org/abs/1907.05600},
  eprinttype    = {arXiv},
  eprint       = {1907.05600},
  timestamp    = {Thu, 01 Oct 2020 17:52:46 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1907-05600.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{Tweedie_Efron,
 ISSN = {01621459},
 URL = {http://www.jstor.org/stable/23239562},
 abstract = {We suppose that the statistician observes some large number of estimates zi, each with its own unobserved expectation parameter μi. The largest few of the zi's are likely to substantially overestimate their corresponding μi's, this being an example of selection bias, or regression to the mean. Tweedie's formula, first reported by Robbins in 1956, offers a simple empirical Bayes approach for correcting selection bias. This article investigates its merits and limitations. In addition to the methodology, Tweedie's formula raises more general questions concerning empirical Bayes theory, discussed here as "relevance" and "empirical Bayes information." There is a close connection between applications of the formula and James—Stein estimation.},
 author = {Bradley Efron},
 journal = {Journal of the American Statistical Association},
 number = {496},
 pages = {1602--1614},
 publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
 title = {Tweedie's Formula and Selection Bias},
 urldate = {2024-04-27},
 volume = {106},
 year = {2011}
}
@misc{kim2021noise2score,
      title={Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images}, 
      author={Kwanyoung Kim and Jong Chul Ye},
      year={2021},
      eprint={2106.07009},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}
@misc{gao2020bounds,
      title={Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions}, 
      author={Xiang Gao and Meera Sitharam and Adrian E. Roitberg},
      year={2020},
      eprint={1712.05267},
      archivePrefix={arXiv},
      primaryClass={math.PR}
}
@article{denosing_fusion,
title = {Multi-focus image fusion with a deep convolutional neural network},
journal = {Information Fusion},
volume = {36},
pages = {191-207},
year = {2017},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2016.12.001},
url = {https://www.sciencedirect.com/science/article/pii/S1566253516302081},
author = {Yu Liu and Xun Chen and Hu Peng and Zengfu Wang},
keywords = {Image fusion, Multi-focus image fusion, Deep learning, Convolutional neural networks, Activity level measurement, Fusion rule},
abstract = {As is well known, activity level measurement and fusion rule are two crucial factors in image fusion. For most existing fusion methods, either in spatial domain or in a transform domain like wavelet, the activity level measurement is essentially implemented by designing local filters to extract high-frequency details, and the calculated clarity information of different source images are then compared using some elaborately designed rules to obtain a clarity/focus map. Consequently, the focus map contains the integrated clarity information, which is of great significance to various image fusion issues, such as multi-focus image fusion, multi-modal image fusion, etc. However, in order to achieve a satisfactory fusion performance, these two tasks are usually difficult to finish. In this study, we address this problem with a deep learning approach, aiming to learn a direct mapping between source images and focus map. To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model, which overcomes the difficulty faced by the existing fusion methods. Based on the above idea, a new multi-focus image fusion method is primarily proposed in this paper. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in terms of both visual quality and objective assessment. The computational speed of the proposed method using parallel computing is fast enough for practical usage. The potential of the learned CNN model for some other-type image fusion issues is also briefly exhibited in the experiments.}
}
@article{Enhancement_fusion,
title = {Multi-exposure image fusion via deep perceptual enhancement},
journal = {Information Fusion},
volume = {79},
pages = {248-262},
year = {2022},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2021.10.006},
url = {https://www.sciencedirect.com/science/article/pii/S1566253521002049},
author = {Dong Han and Liang Li and Xiaojie Guo and Jiayi Ma},
keywords = {Multi-exposure image fusion, Perceptual enhancement, Contrast enhancement, Color correction, Illumination adjustment},
abstract = {Due to the huge gap between the high dynamic range of natural scenes and the limited (low) range of consumer-grade cameras, a single-shot image can hardly record all the information of a scene. Multi-exposure image fusion (MEF) has been an effective way to solve this problem by integrating multiple shots with different exposures, which is in nature an enhancement problem. During fusion, two perceptual factors including the informativeness and the visual realism should be concerned simultaneously. To achieve the goal, this paper presents a deep perceptual enhancement network for MEF, termed as DPE-MEF. Specifically, the proposed DPE-MEF contains two modules, one of which responds to gather content details from inputs while the other takes care of color mapping/correction for final results. Both extensive experimental results and ablation studies are conducted to show the efficacy of our design, and demonstrate its superiority over other state-of-the-art alternatives both quantitatively and qualitatively. We also verify the flexibility of the proposed strategy on improving the exposure quality of single images. Moreover, our DPE-MEF can fuse 720p images in more than 60 pairs per second on an Nvidia 2080Ti GPU, making it attractive for practical use. Our code is available at https://github.com/dongdong4fei/DPE-MEF.}
}
@article{multi_fusion,
  author    = {Nasrin Amini and Ahmad Mostaar},
  title     = {Deep Learning Approach for Fusion of Magnetic Resonance Imaging-Positron Emission Tomography Image Based on Extract Image Features using Pretrained Network (VGG19)},
  journal   = {Journal of Medical Signals \& Sensors},
  volume    = {12},
  number    = {1},
  pages     = {25--31},
  year      = {2022},
  month     = {Jan--Mar},
  doi       = {10.4103/jmss.JMSS_80_20}
}
@article{Fusion_DWt,
title = {Multisensor Image Fusion Using the Wavelet Transform},
journal = {Graphical Models and Image Processing},
volume = {57},
number = {3},
pages = {235-245},
year = {1995},
issn = {1077-3169},
doi = {https://doi.org/10.1006/gmip.1995.1022},
url = {https://www.sciencedirect.com/science/article/pii/S1077316985710222},
author = {H. Li and B.S. Manjunath and S.K. Mitra},
abstract = {The goal of image fusion is to integrate complementary information from multisensor data such that the new images are more suitable for the purpose of human visual perception and computer-processing tasks such as segmentation, feature extraction, and object recognition. This paper presents an image fusion scheme which is based on the wavelet transform. The wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transform of the fused wavelet coefficients. An area-based maximum selection rule and a consistency verification step are used for feature selection. The proposed scheme performs better than the Laplacian pyramid-based methods due to the compactness, directional selectivity, and orthogonality of the wavelet transform. A performance measure using specially generated test images is suggested and is used in the evaluation of different fusion methods, and in comparing the merits of different wavelet transform kernels. Extensive experimental results including the fusion of multifocus images, Landsat and Spot images, Landsat and Seasat SAR images, IR and visible images, and MRI and PET images are presented in the paper.}
}
@article{Pixelbased_fusion,
title = {Pixel- and region-based image fusion with complex wavelets},
journal = {Information Fusion},
volume = {8},
number = {2},
pages = {119-130},
year = {2007},
note = {Special Issue on Image Fusion: Advances in the State of the Art},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2005.09.006},
url = {https://www.sciencedirect.com/science/article/pii/S1566253505000862},
author = {John J. Lewis and Robert J. O’Callaghan and Stavri G. Nikolov and David R. Bull and Nishan Canagarajah},
keywords = {Image fusion, Feature-based fusion, Region-based fusion, Segmentation, Complex wavelets},
abstract = {A number of pixel-based image fusion algorithms (using averaging, contrast pyramids, the discrete wavelet transform and the dual-tree complex wavelet transform (DT-CWT) to perform fusion) are reviewed and compared with a novel region-based image fusion method which facilitates increased flexibility with the definition of a variety of fusion rules. A DT-CWT is used to segment the features of the input images, either jointly or separately, to produce a region map. Characteristics of each region are calculated and a region-based approach is used to fuse the images, region-by-region, in the wavelet domain. This method gives results comparable to the pixel-based fusion methods as shown using a number of metrics. Despite an increase in complexity, region-based methods have a number of advantages over pixel-based methods. These include: the ability to use more intelligent semantic fusion rules; and for regions with certain properties to be attenuated or accentuated.}
}
@article{imagefusiontuto,
title = {A wavelet-based image fusion tutorial},
journal = {Pattern Recognition},
volume = {37},
number = {9},
pages = {1855-1872},
year = {2004},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2004.03.010},
url = {https://www.sciencedirect.com/science/article/pii/S0031320304001037},
author = {Gonzalo Pajares and Jesús {Manuel de la Cruz}},
keywords = {Image fusion, Wavelets, Multiresolution},
abstract = {The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.}
}
@inproceedings{LPIPS,
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A and Shechtman, Eli and Wang, Oliver},
  booktitle={CVPR},
  year={2018}
}
@article{sift,
  title={Distinctive Image Features from Scale-Invariant Keypoints},
  author={Lowe, David G.},
  journal={International Journal of Computer Vision},
  volume={60},
  number={2},
  pages={91--110},
  year={2004},
  publisher={Kluwer Academic Publishers},
  doi={10.1023/B:VISI.0000029664.99615.94},
  url={https://doi.org/10.1023/B:VISI.0000029664.99615.94}
}
@misc{vgg,
      title={Very Deep Convolutional Networks for Large-Scale Image Recognition}, 
      author={Karen Simonyan and Andrew Zisserman},
      year={2015},
      eprint={1409.1556},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{resnet,
      title={Deep Residual Learning for Image Recognition}, 
      author={Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
      year={2015},
      eprint={1512.03385},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{SRGAN,
      title={Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network}, 
      author={Christian Ledig and Lucas Theis and Ferenc Huszar and Jose Caballero and Andrew Cunningham and Alejandro Acosta and Andrew Aitken and Alykhan Tejani and Johannes Totz and Zehan Wang and Wenzhe Shi},
      year={2017},
      eprint={1609.04802},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{SRCNN,
      title={Image Super-Resolution Using Deep Convolutional Networks}, 
      author={Chao Dong and Chen Change Loy and Kaiming He and Xiaoou Tang},
      year={2015},
      eprint={1501.00092},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@ARTICLE{SSIM,
  author={Zhou Wang and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P.},
  journal={IEEE Transactions on Image Processing}, 
  title={Image quality assessment: from error visibility to structural similarity}, 
  year={2004},
  volume={13},
  number={4},
  pages={600-612},
  keywords={Image quality;Humans;Transform coding;Visual system;Visual perception;Data mining;Layout;Quality assessment;Degradation;Indexes},
  doi={10.1109/TIP.2003.819861}}

@InProceedings{CicGAN,
author = {Yuan, Yuan and Liu, Siyuan and Zhang, Jiawei and Zhang, Yongbing and Dong, Chao and Lin, Liang},
title = {Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
@article{PCA,
  title={Principal component analysis: a review and recent developments},
  author={Jolliffe, I. T. and Cadima, J.},
  journal={Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  volume={374},
  number={2065},
  pages={20150202},
  year={2016},
  publisher={The Royal Society},
  doi={10.1098/rsta.2015.0202},
  url={https://doi.org/10.1098/rsta.2015.0202}
}
@misc{biggan,
      title={Large Scale GAN Training for High Fidelity Natural Image Synthesis}, 
      author={Andrew Brock and Jeff Donahue and Karen Simonyan},
      year={2019},
      eprint={1809.11096},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1809.11096}, 
}
@article{IVLR,
  author       = {Jooyoung Choi and
                  Sungwon Kim and
                  Yonghyun Jeong and
                  Youngjune Gwon and
                  Sungroh Yoon},
  title        = {{ILVR:} Conditioning Method for Denoising Diffusion Probabilistic
                  Models},
  journal      = {CoRR},
  volume       = {abs/2108.02938},
  year         = {2021},
  url          = {https://arxiv.org/abs/2108.02938},
  eprinttype    = {arXiv},
  eprint       = {2108.02938},
  timestamp    = {Mon, 24 Apr 2023 14:58:38 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2108-02938.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@ARTICLE{bicubic,
  author={Keys, R.},
  journal={IEEE Transactions on Acoustics, Speech, and Signal Processing}, 
  title={Cubic convolution interpolation for digital image processing}, 
  year={1981},
  volume={29},
  number={6},
  pages={1153-1160},
  keywords={Convolution;Interpolation;Digital images;Kernel;Image sampling;Sampling methods;Signal processing algorithms;Image processing;Image converters;Boundary conditions},
  doi={10.1109/TASSP.1981.1163711}}
@article{ZSRGAN,
  author       = {Assaf Shocher and
                  Nadav Cohen and
                  Michal Irani},
  title        = {"Zero-Shot" Super-Resolution using Deep Internal Learning},
  journal      = {CoRR},
  volume       = {abs/1712.06087},
  year         = {2017},
  url          = {http://arxiv.org/abs/1712.06087},
  eprinttype    = {arXiv},
  eprint       = {1712.06087},
  timestamp    = {Mon, 13 Aug 2018 16:47:16 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1712-06087.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@misc{realESRGAN,
      title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data}, 
      author={Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
      year={2021},
      eprint={2107.10833},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2107.10833}, 
}
@misc{blindDPS,
      title={Parallel Diffusion Models of Operator and Image for Blind Inverse Problems}, 
      author={Hyungjin Chung and Jeongsol Kim and Sehui Kim and Jong Chul Ye},
      year={2022},
      eprint={2211.10656},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2211.10656}, 
}

@book{LanczosFilteringinOneandTwoDimensions,
author = {Wolberg, George},
address = {Los Alamitos, Calif},
booktitle = {Digital image warping},
isbn = {0818689447},
keywords = {Image processing -- Digital techniques},
language = {eng},
lccn = {91040320 //r92},
publisher = {IEEE Computer Society Press},
series = {IEEE Computer Society Press monograph},
title = {Digital image warping / George Wolberg.},
year = {1992 - 1990},
}
@misc{kernelgan,
      title={Blind Super-Resolution Kernel Estimation using an Internal-GAN}, 
      author={Sefi Bell-Kligler and Assaf Shocher and Michal Irani},
      year={2020},
      eprint={1909.06581},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1909.06581}, 
}
@article{DSCR,
author = {Zhang, Zhendong and Wang, Xinran and Jung, Cheolkon},
year = {2018},
month = {10},
pages = {1-1},
title = {DCSR: Dilated Convolutions for Single Image Super-Resolution},
volume = {PP},
journal = {IEEE Transactions on Image Processing},
doi = {10.1109/TIP.2018.2877483}
}
@article{DLSSS,
author = {de Leeuw den Bouter, Merel and Ippolito, G. and O'Reilly, Thomas and Remis, Robert and Gijzen, M.B. and Webb, Andrew},
year = {2022},
month = {04},
pages = {6362},
title = {Deep learning-based single image super-resolution for low-field MR brain images},
volume = {12},
journal = {Scientific Reports},
doi = {10.1038/s41598-022-10298-6}
}