Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

Junxiong Lin, Zeng Tao, Xuan Tong, Xinji Mai, Haoran Wang, Boyang Wang, Yan Wang, Qing Zhao, Jiawen Yu, Yuxuan Lin, Shaoqi Yan, Shuyong Gao, Wenqiang Zhang

Abstract: The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an \textbf{U}ncertainty-based degradation representation for blind \textbf{S}uper-\textbf{R}esolution framework (\textbf{USR}). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. Rhe VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach.

Primary Subject Area: [Experience] Multimedia Applications

Secondary Subject Area: [Experience] Interactions and Quality of Experience

Relevance To Conference:

This work contributes significantly to the field of multimedia and multimodal processing by advancing the capabilities of image super-resolution (SR) techniques, a cornerstone technology for enhancing visual content. By improving the resolution of images, this research directly impacts various applications such as digital forensics, medical imaging, and video streaming services, where clarity and detail are paramount. High-quality SR enables the extraction and analysis of more accurate information from images and videos, facilitating better decision-making and user experiences.

Moreover, the advancements in image SR techniques bolster the performance of multimodal systems that rely on visual data. Enhanced image resolution improves the effectiveness of algorithms in object recognition, scene understanding, and visual question answering, which are critical for the development of comprehensive multimedia systems. By integrating improved SR methods, these systems can offer richer and more interactive experiences, bridging the gap between different types of media and modalities.

Furthermore, the consistent presence of SR research in annual conferences underscores its ongoing relevance and the vibrant community of scholars dedicated to pushing the boundaries of image quality improvement. This work lays a foundation for future research in multimedia processing, encouraging the exploration of novel algorithms and applications that can leverage high-resolution imagery. The convergence of high-quality image SR with other multimedia processing technologies paves the way for innovative applications that can transform the way we interact with digital content across various platforms.

Submission Number: 45