HcaNet: Haze-concentration-aware Network for Real-scene Dehazing with Codebook Priors
Abstract: In the task of image dehazing, it has been proven that high-quality codebook priors can be used to compensate for the distribution differences between real-world hazy images and synthetic hazy images, thereby helping the model improve its performance. However, because the concentration and distribution of haze in the image are irregular, the manners those simply replacing or blending the prior information in the codebook with the original image features are inconsistent with this irregularity, which leads to a non-ideal dehazing performance. To this end, we propose a haze concentration aware network(HcaNet), its haze-concentration-aware module(HcaM) can reduce the information loss in the vector quantization stage and achieve an adaptive domain transfer for regions with different degrees of degradation. To further capture the detailed texture information, we develop a frequency selective fusion module(FSFM) to facilitate the transmission of shallow information retained in haze areas to deeper layers, thereby enhancing the fusion with high-quality feature priors. Extensive evaluations demonstrate that the proposed model can be merely trained on synthetic hazy-clean pairs and effectively generalize to real-world data. Several experimental results confirm that the proposed dehazing model outperforms state-of-the-art methods significantly on real-world images.
Primary Subject Area: [Content] Media Interpretation
Relevance To Conference: This work on real-world dehazing contributes to multimedia/multimodal processing by improving visual quality, restoring images and videos, enhancing scene understanding, and enhancing user experience. By developing effective dehaze techniques, it addresses the challenges of haze and fog in real-world scenarios, resulting in better clarity, contrast, and details in multimedia content. It enables accurate object recognition, tracking, and segmentation, and facilitates cross-modal integration with depth information or other modalities for more robust multimedia analysis. Overall, this work advances the capabilities of multimedia processing, benefiting applications such as photography, video surveillance, autonomous vehicles, and immersive multimedia experiences.
Submission Number: 3354
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