Research on the Enhancement Algorithm of Defocused and Blurred Image Base on Non-local Constraints

Abstract

Due to the diversity of defocus blurred images, causing the unsatisfied effect of sharpness enhancement of the defocused and blurred image. On this basis, the paper has proposed an enhancement algorithm of defocused and blurred image base on non-local constraints. The estimated results similar to the original image was acquired, the actual defocus blur optical transfer function was calculated and the low inverter over-amplifying the noise was avoided through the improvement of inverse filtering by the Wiener filter. Based on the symmetry of a defocused and blurred circular, the diffusion curve of straight marginal function was filtered and restored to eliminate the noise in the image. On this basis, the non-local self-similarity and total variational regularity of the defocused and blurred image were complemented, and the sharpness of the defocused and blurred image was finally enhanced by using the non-local model to restore the marginal and detailed texture information of the defocused and blurred image. The results of the simulation have shown that the proposed method could not only increase the computational efficiency, but also obtain satisfactory sharpness enhancement of the defocused and blurred image, and a better retain effect of the marginal information.