In order to solve these defects such as low visual quality of restoration image induced by over-smoothing textured areas resulting in eliminating image texture in current image restoration algorithms, the image restoration algorithm based on TV-Poisson singular integral joint priori model coupled with Bayesian inference was proposed. TV image prior was constructed by introducing partition function and combining TV function. A new joint priori model was designed by introducing defining and embedding the Poisson singular integral prior to control the smooth degree of image texture. The image degradation model was built based on the high-order statistics technique, and coupled with the priori model to produce the Maximizing A Posteriori. The reconstruction image was obtained by performing the Bayesian inference under the condition of using majorization-minimization principle to solve the MAP. Additionally, optimization of key parameters for image texture restoration with the proposed algorithm was conducted and the user responses of this algorithm were analyzed. In comparison with the current image reconstruction mechanism, the algorithm proposed in this paper had higher restoration visual quality which can better balance the noise and texture. Besides, good user responses of this algorithm was obtained when the image degradation degree was large.