The work aims to solve the problem that the image inpainting algorithm ignores the subsequent optimization of the patch, leading to the lack of coherent effect and block effect in the inpainting image. An image inpainting algorithm based on texture features and sparse representation was proposed. Firstly, the priority model was constructed with the data item corresponding to the pixel points. Then, the texture feature measurement model was constructed with the pixel values corresponding to the pixel points on components R, G and B to measure the texture features corresponding to pixel points in the block to be restored. Moreover, the size of its corresponding sample set was selected according to the measurement results. Through the introduction of SSD model, the optimal sample block that was the most similar to the block to be repaired was searched from the sample set and used to fill the block to be repaired. Finally, the optimal sparse representation model was constructed with the optimal sample block function to achieve image inpainting. The simulation results showed that, compared with the current image inpainting algorithm, the proposed image inpainting algorithm had higher recovery quality and could effectively overcome the incoherent effect and block effect in inpainting. The proposed algorithm has higher visual quality of inpainting and better application value in the field of digital image processing..