A mine low illumination image enhancement algorithm

The wavelet transform cannot maintain a balance in image edge preservation and detail processing and the multi-scale Retinex algorithm is prone to halo artifacts and serious noise pollution in images. In order to solve the above problems, a mine low illumination image enhancement algorithm is proposed by combining the wavelet transform with the multi-scale Retinex algorithm based on multi-scale guided filtering. Firstly, the algorithm decomposes the low illumination image into wavelets to obtain the high-frequency and low-frequency components. Secondly, the wavelet denoising is applied to the high-frequency components of the image using a three-stage threshold function, and nonlinear global luminance correction is applied to the low-frequency components of the image to enhance the image luminance. Moreover, the multi-scale guided filter function is used to estimate the illumination components instead of the Gaussian filter function of the traditional multi-scale Retinex algorithm, and then the reflection components are obtained. The principal component analysis method is used to fuse the reflection component and the non-linear global luminance correction image so as to improve image edge detail preservation effect effectively. Finally, the wavelet reconstruction is performed on the high-frequency components and low-frequency components of the image, and the wavelet reconstructed image is nonlinearly transformed to solve the image graying problem. The experimental results show that the algorithm has strong noise suppression capability, can improve the image luminance and contrast effectively, make the image edge preservation performance and detail information richness effectively balanced, and avoid the image halo artifacts and color distortion.