## 1 Introduction  
Image dehazing plays a pivotal role in computer vision tasks by restoring the clarity of images degraded by atmospheric phenomena such as fog, haze, or smog [1]. The presence of these environmental factors significantly hampers the visibility and perceptibility of important image details, often leading to severe reductions in contrast, color fidelity, and overall image quality. This degradation poses substantial challenges for visual recognition systems, particularly in critical applications such as autonomous driving [2], target tracking [3, 4, 5], and outdoor object detection [6, 7, 8], where accurate and detailed scene understanding is paramount. The atmospheric scattering model [9, 10] provides insights into the formation of hazy images by explaining how haze affects an image through two main components: the scene radiance that is attenuated by the medium and the atmospheric light that is scattered into the camera. The observed hazy image is a combination of these two factors. The transmission map represents the extent to which the scene radiance is preserved after passing through the haze, with denser haze leading to lower transmission values. Meanwhile, the atmospheric light refers to the ambient light scattered by particles in the air, which contributes to the overall brightness and color shift in the hazy image. Most of the previous prior-based studies realized image dehazing by predicting transmission maps and atmospheric light. For instance, [PERSON] et al. [11] developed the Dark Channel Prior (DCP) as a technique for estimating the transmission map. [PERSON] et al. [12] proposed a method for estimating the transmission map by leveraging prior information on object surface-shading and transmission characteristics. These methods based on prior knowledge can produce dehazed images to some extent. However, if the assumed priors do not align well with a particular scene or the parameter estimation is imprecise, the resulting images may exhibit artifacts, color distortion, or other undesired effects. In contrast, deep learning algorithms leverage the powerful feature extraction and nonlinear mapping capabilities of convolutional neural networks (CNNs) to enhance the accuracy of parameter estimation, enabling dehazing models to produce clear, haze-free images autonomously, eliminating the reliance on explicit physical models, thereby driving the rapid advancement of image-dehazing technologies. In recent years, the rapid development of deep learning has led to significant breakthroughs in computer vision [13; 14; 15]. Many dehazing approaches based on these methods have shown remarkable success. Some approaches employ convolutional neural networks to predict the transmission map and atmospheric light [16; 17; 18], while others directly learn various features to generate haze-free images [19; 20; 21; 22]. Because these learning-based methods can handle large training datasets, they effectively extract useful information, resulting in high-quality, clear outputs even in hazy conditions. Many dehazing methods focus on enhancing image contrast and clarity while overlooking the precise restoration of color information. As a result, dehazed images may exhibit color distortion and bias, leading to suboptimal visual quality [23; 24; 25]. Figure 1 shows examples of color difference in image dehazing. Color plays a pivotal role in image processing; accurate color representation not only influences the visual experience but also significantly impacts subsequent image analysis and computer vision tasks. [PERSON] et al. [26] proposed a local adaptive-fusion strategy that constructs a light attenuation map through the red channel to quantify local attenuation, then fuses the original image with a globally adjusted color map to dynamically correct the intensity. [PERSON] et al. [27] first preprocessed the images using a white balance algorithm and a DCP dehazing technique, and applied a multiscale fusion strategy to correct color deviations.