School of Information Engineering (School of Big Data), Xuzhou University of Technology, Xuzhou, Jiangsu, China.
Traffic Police Detachment of Xuzhou Public Security Bureau, Xuzhou, Jiangsu, China.
J Environ Public Health. 2022 Jul 7;2022:3887426. doi: 10.1155/2022/3887426. eCollection 2022.
According to the characteristics of foggy images, such as high noise, low resolution, and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed. First, the new algorithm refines the transmittance and optimizes the atmospheric light value and converts the restored image to HSV space. Second, the brightness component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The new method solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion in JPEG, BMP, GIF, PNG, PSD, and TIFF formats. By comparing with other image enhancement algorithms, the improved algorithm performs better than DCP, SSR, MSR, MSRCR, and CLAHE algorithm in PSNR, SSIM, and IE evaluation indexes. It has a good effect on preserving the edge information and has good adaptability and stability for heavily polluted haze image enhancement.
针对雾天图像噪声大、分辨率低、光照不均匀等特点，提出了一种基于暗通道先验的改进雾天图像增强方法。首先，新算法对透射率进行细化，优化大气光值，并将恢复后的图像转换到 HSV 空间。其次，通过双边滤波改进的 MSRCR 算法对亮度分量进行增强，自适应拉伸算法对饱和度 S 进行增强。最后，将图像从 HSV 空间转换到 RGB 空间，完成图像增强。新方法解决了传统暗通道先验方法去雾时大面积颜色不均匀、图像整体偏暗的问题。实验结果表明，从主观评价和定量分析两方面来看，新算法克服了传统增强算法增强图像时存在的噪声放大和边缘模糊的缺点。它可以改善图像变暗，避免 JPEG、BMP、GIF、PNG、PSD 和 TIFF 格式的图像失真。通过与其他图像增强算法进行比较，改进后的算法在 PSNR、SSIM 和 IE 评价指标上优于 DCP、SSR、MSR、MSRCR 和 CLAHE 算法。它对边缘信息的保留效果较好，对重度污染雾霾图像增强具有良好的适应性和稳定性。
