Image Haze Removal Using Dual Dark Channels with the Whale Optimization Algorithm and an Image Regression Model

Recently, image haze removal has gained increasing attention in the field of image restoration. Data-driven and model-based methods are popular among researchers. The dark channel prior prevails in model-based methods, where the model parameters, atmospheric light, and transmittance are generally estimated through a block-based dark channel. This paper proposes a model-based approach with integrated pixel- and block-based dark channels for initial transmittance estimation. Additionally, we developed a two-stage guided image filtering process to refine the initial transmittance while utilizing the pixel-based dark channel to estimate atmospheric light. Our approach introduces two scaling factors for atmospheric light and initial transmittance, which are optimized using the Whale Optimization Algorithm. A CNN image regression model is employed to learn the mapping between hazy images and their corresponding optimized scaling factors, thus eliminating the need for ground-truth images. This makes our approach applicable in real-world scenarios. The proposed approach was validated using two datasets: an artificially generated image dataset, RESIDE, and a natural image dataset, KeDeMa. The results show that our approach outperforms four other dehazing methods, i.e., GCAN, RRO, RFDN, and Ka-Net. With the RESIDE dataset, our approach outperforms GCAN, RRO, RFD, and Ka-Net by 2.009 dB, 6.042 dB, 3.488 dB, and 8.975 dB, respectively, in terms of PSNR. With the KeDeMa dataset, our approach generally demonstrates superior visual quality to the four comparison methods. The results suggest that the proposed model-based approach may outperform data-driven methods.