CVPR 2023 - RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

In this episode we discuss RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors by Authors: 1. Rui-Qi Wu 2. Zheng-Peng Duan 3. Chun-Le Guo 4. Zhi Chai 5. Chongyi Li Affiliations: 1. VCIP, CS, Nankai University 2. Hisilicon Technologies Co. Ltd. 3. S-Lab, Nanyang Technological University. The paper discusses a new approach to real image dehazing, which addresses the challenges of existing methods that struggle to process real-world hazy images due to the lack of paired real data and robust priors. The proposed method synthesizes more realistic hazy data and introduces more robust priors into the network. The approach includes a phenomenological pipeline that considers diverse degradation types and a Real Image Dehazing network via high-quality Codebook Priors (RIDCP) that utilizes a VQGAN pre-trained on a large-scale high-quality dataset to obtain the discrete codebook encapsulating high-quality priors. Extensive experiments confirm the effectiveness of the proposed approach.