Image restoration based on sparse representation using feature classification learning

In the image inpainting method based on sparse representation, the adaptability of over-complete dictionary has a great influence on the result of image restoration. If the over-complete dictionary cannot effectively reflect the differences between different local features, it may result in the loss of texture details, resulting in blurred or over-smooth phenomenon in restored images. In view of these problems, we propose an image restoration method based on sparse representation using feature classification learning. Firstly, we perform singular value decomposition on the local gradient vector. According to the relationship between the main orientation and the secondary orientation, we classify all the local patches into three categories: smooth patch, edge patch and texture patch. Secondly, we use K-Singular Value Decomposition method to learn over-complete dictionaries that adapt to different features. Finally, we use Orthogonal Matching Pursuit method to calculate the sparse coding of target patches with different local features on their corresponding over-complete dictionaries, and use the over-complete dictionary and corresponding sparse coding to restore the damaged pixels. A series of experiments on various restoration tasks show the superior performance of the proposed method.