Defogging Algorithm for Road Environment Landscape Visual Image Based on Wavelet Transform

For the problem of serious image degradation and low contrast in foggy weather, an improved algorithm is proposed. In order to obtain important information of foggy images, an image dehazing method based on wavelet transform is proposed. Perform a layer of wavelet decomposition on the foggy image, perform single-scale enhancement and homomorphic filtering on the low-frequency image, and then linearly combine the two processed low-frequency images to obtain a new low-frequency image, and finally combine the new low-frequency image with the unprocessed image. The high-frequency image is reconstructed by inverse wavelet transform. First, the three color channels of the image are processed by histogram equalization in the color space; at the same time, the components are extracted in the color space, and two-dimensional discrete wavelet transform is performed on them to obtain a low-frequency component and three high-frequency components. The three high-frequency components are subjected to limited contrast histogram equalization processing, and then the two-dimensional discrete wavelet inverse transform is performed to reconstruct the high and low frequency parts, and finally converted back to the color space, and the two images are linearly combined to obtain the final dehaze image. From the perspective of time-frequency analysis, the Fourier transform in the homomorphic filtering algorithm is replaced by the fast wavelet transform, and then the wavelet coefficients are processed with the improved filter in the transform city. Keep the original information of the image not lost, enhance the contrast of the image, and make the processed image more visible. Compared with other dehazing methods, the image processed by this method has higher definition, faster time, higher contrast, better visual sense, higher brightness, more realistic color recovery, and better dehazing effect.