What are the challenges in haze removal in color compensation?
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Haze removal in color compensation faces several challenges. Firstly, hazy images often exhibit low contrast, color shift, and structural distortion, making it difficult to recover structural and chromatic features with high fidelity, especially in regions with heavy haze . Secondly, existing small-scale datasets for non-homogeneous dehazing are insufficient for reliable learning of feature mappings between hazy images and their haze-free counterparts using convolutional neural network (CNN)-based models . These challenges arise due to the intricate and non-uniform distribution of dense haze, which affects the recovery of image details and coherence between image patches . To address these challenges, novel methods have been proposed, such as leveraging 2D discrete wavelet transform (DWT), fast Fourier convolution (FFC) residual blocks, and pretrained ConvNeXt models to capture high-frequency features, explore global contextual information, and learn supplementary information for better perceptual quality .
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07 May 2023 | The provided paper does not specifically mention the challenges in haze removal in color compensation. |
| The provided paper does not specifically mention the challenges in haze removal in color compensation. | |
| The paper does not mention any challenges specifically related to color compensation in haze removal. | |
| The paper does not mention any challenges specifically related to color compensation in haze removal. | |
12 Oct 2022 | The provided paper does not mention any challenges specifically related to haze removal in color compensation. |
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