Design of a two-branch network enhancement algorithm for deep features in visually communicated images

Aiming at the problem that the visual communication image enhances the features from a single angle, resulting in the fuzzy phenomenon of some details, a double branch network enhancement algorithm for deep features of visual communication image is proposed. The image is preprocessed by bilateral filtering, and then the global noise level of the image is obtained by blind estimation, which is used as the residual threshold in the sparse reconstruction process, and the image filtering processing is realized by combining sparse coding and dictionary learning algorithm; Calculate the image saliency value, establish the saliency matrix, divide the foreground area and background area of the image, and use the histogram equalization method to carry out histogram equalization processing on the foreground and background area respectively to realize the global enhancement of the visual communication image; The enhanced image is input into the dual branch network, and the spatial domain information and the frequency domain information after Fourier transform of the image are processed in the spatial domain branch and the frequency domain branch respectively, and the features of the two branches are guided to conduct adaptive fusion through the attention mechanism to obtain the final enhanced image. Experimental results show that the proposed method has good image denoising and enhancement effects.