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<p>Foggy images often suffer from poor visibility due to low contrast and high noise, which can impede effective image analysis. To address this issue, we propose a novel image enhancement technique based on Contrast Limited Adaptive Histogram Equalization (CLAHE), designed to improve the contrast and reduce the noise in foggy images. The method is implemented in two phases: in the first phase, CLAHE is applied to the Red-Green-Blue (RGB) colour model, specifically targeting the red channel, which is most affected by scattering. In the second phase, CLAHE is applied to the Hue-Saturation-Value (HSV) model, with adjustments made to the saturation and value components, while keeping the hue unaffected. The results from both phases are then combined to produce the final enhanced image. Experimental evaluation of the proposed method using Root Mean Squared Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) metrics shows significant improvements over existing enhancement techniques. As indicated by the experimental results, the proposed method achieves the lowest RMSE values (10.927 for Img2) and the highest PSNR values (31.12 for Img2), demonstrating its ability to minimize noise and error while enhancing image quality. Compared to traditional methods such as Histogram Equalization, Dehazing, and Percentile-based approaches, the proposed technique offers superior contrast enhancement and detail preservation in foggy conditions. These findings confirm the efficacy of the proposed CLAHE-based method in improving the quality of foggy images, making it a promising solution for various applications requiring high-quality image analysis in challenging visibility conditions.</p>

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CLAHE-Based Contrast Improvement and Noise Reduction for Foggy Images

  • 期刊论文

  • 发表日期：

    2025年10月16日

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  • Dandan Zhang,
  • Ahmed Alkhayyat,
  • Gadug Sudhamsu,
  • Prabhat Kumar Sahu,
  • Shivakrishna Dasi,
  • Ankur Srivastava,
  • Devendra Singh,
  • J. Albert Mayan

摘要

Foggy images often suffer from poor visibility due to low contrast and high noise, which can impede effective image analysis. To address this issue, we propose a novel image enhancement technique based on Contrast Limited Adaptive Histogram Equalization (CLAHE), designed to improve the contrast and reduce the noise in foggy images. The method is implemented in two phases: in the first phase, CLAHE is applied to the Red-Green-Blue (RGB) colour model, specifically targeting the red channel, which is most affected by scattering. In the second phase, CLAHE is applied to the Hue-Saturation-Value (HSV) model, with adjustments made to the saturation and value components, while keeping the hue unaffected. The results from both phases are then combined to produce the final enhanced image. Experimental evaluation of the proposed method using Root Mean Squared Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) metrics shows significant improvements over existing enhancement techniques. As indicated by the experimental results, the proposed method achieves the lowest RMSE values (10.927 for Img2) and the highest PSNR values (31.12 for Img2), demonstrating its ability to minimize noise and error while enhancing image quality. Compared to traditional methods such as Histogram Equalization, Dehazing, and Percentile-based approaches, the proposed technique offers superior contrast enhancement and detail preservation in foggy conditions. These findings confirm the efficacy of the proposed CLAHE-based method in improving the quality of foggy images, making it a promising solution for various applications requiring high-quality image analysis in challenging visibility conditions.