Double-channel cyclic image deblurring algorithm based on edge features

Photographs taken in daily life often became blurred due to shaking, out-of-focus, changes in depth of field, and movement of photographed objects. Aiming at this problem, a double-channel cyclic image deblurring method based on edge features was proposed. Firstly, image edge gradient operator was introduced as a threshold based on the rule that the maximum value of the image edge gradient will decrease after the blurring process, making the blurred image be divided into two channels: edge channel and non-edge channel. Secondly, a double-channel loop iteration network was designed, where the edge gradient was used in the edge channel to sample the main edge structure and bilateral filtering was used in the non-edge channel to extract the detailed texture feature information. Finally, the feature information extracted from two channels was cyclically iterated to obtain a clear image using the deblurring model with maximum a posteriori probability. The experimental results showed that the image evaluation indexes obtained by the proposed deblurring model were superior to those of other algorithms, and the edge structure and texture details of the image were effectively recovered with better performance.