Abstract: In order to intuitively and accurately eliminate the image blur phenomenon caused by the defocus error and improve the accuracy of digital image processing, a fuzzy image visual effect optimization algorithm based on stereo binocular vision is proposed. The fuzzy image optimization problem is transformed into an optimization estimation problem under the constraint of the fuzzy core path relationship. Based on the fuzzy core path correspondence, a stereo binocular image fuzzy core path corresponding relationship model is constructed, and the model is used as a convergence condition to obtain the coupling between the fuzzy image features relation and three-dimensional geometric information. Directional filter is used to remove fuzzy image noise, Radon transform is used to deal with the error caused by directional filter in fuzzy function estimation, convolution operation is used to remove blur phenomenon, through the gradient norm’s variational functional optimization algorithm. The visual effects of blurred images is optimized. The experimental results show that the proposed method can optimize the image contaminated by noise and blur, and retain the texture and detail information of the image completely. When the number of iterations is 7 times, the difference energy of the image reaches convergence, and the visual effect of the blurred image can be completed efficiently optimization.

Key words: stereo binocular vision, blurred image, visual effect optimization, feature analysis, blur function estimation