Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13147 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912299921440768 |
|---|---|
| author | Fu, Jiayi Liu, Siyu Liu, Zikun Guo, Chun-Le Park, Hyunhee Wu, Ruiqi Wang, Guoqing Li, Chongyi |
| author_facet | Fu, Jiayi Liu, Siyu Liu, Zikun Guo, Chun-Le Park, Hyunhee Wu, Ruiqi Wang, Guoqing Li, Chongyi |
| contents | We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance. Our idea stems from the observations that 1) the degradation of hazy images varies with haze density and scene depth, and 2) clear regions play crucial cues in restoring dense haze regions. However, it is non-trivial to progressively refine the obtained codes in subsequent iterations, owing to the difficulty in determining which codes should be retained or replaced at each iteration. Another key insight of our study is to propose Code-Critic to capture interrelations among codes. The Code-Critic is used to evaluate code correlations and then resample a set of codes with the highest mask scores, i.e., a higher score indicates that the code is more likely to be rejected, which helps retain more accurate codes and predict difficult ones. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods in real-world dehazing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing Fu, Jiayi Liu, Siyu Liu, Zikun Guo, Chun-Le Park, Hyunhee Wu, Ruiqi Wang, Guoqing Li, Chongyi Computer Vision and Pattern Recognition We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteration to guide the prediction of the Code-Predictor in the subsequent iteration, improving code prediction accuracy and ensuring stable dehazing performance. Our idea stems from the observations that 1) the degradation of hazy images varies with haze density and scene depth, and 2) clear regions play crucial cues in restoring dense haze regions. However, it is non-trivial to progressively refine the obtained codes in subsequent iterations, owing to the difficulty in determining which codes should be retained or replaced at each iteration. Another key insight of our study is to propose Code-Critic to capture interrelations among codes. The Code-Critic is used to evaluate code correlations and then resample a set of codes with the highest mask scores, i.e., a higher score indicates that the code is more likely to be rejected, which helps retain more accurate codes and predict difficult ones. Extensive experiments demonstrate the superiority of our method over state-of-the-art methods in real-world dehazing. |
| title | Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13147 |