Defogging Algorithm Based on Image Features and Wavelet Transform

Aiming at the problems of halo artifact, dark distortion and detail loss in traditional dark channel prior defogging algorithms, a defogging algorithm based on image features and wavelet transform is proposed. First, The gray-level co-occurrence matrix method is introduced to obtain the complexity of image texture features as a constraint condition, and the problem of false texture and blocking effect in dark channel images is solved by use of dynamic sliding window; second, combined with the image brightness information, K-Means clustering algorithm is used to calibrate the bright and dark areas to optimize the atmospheric light value and transmittance map; finally, aiming at the problems of darkening and loss of detail features in the restored image of atmospheric scattering model, the image enhancement technology based on wavelet transform is used to improve the image contrast. The experimental results show that the proposed algorithm can recover the scence and detail features well, and performs well in peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE).