Simultaneous image deblurring and inpainting via convolutional sparse coding
Shengbiao Wang, Bolun Chen, Huasong Chen, Shixuan Gao, Junhao Wang, Qiansheng Feng, Tianlong Ma · 2021
Image deblurring and inpainting are traditional image processing problems, and the effects achieved for high-resolution images are not satisfactory. In recent years, Convolutional Sparse coding (CSC) has been received more attention and introduced into image processing, such as blind deblurring. However, none of the works address the issue containing both blur and inpainting. In this work, we propose a novel framework of CSC for simultaneous image deblurring and inpainting. First, we learn a dictionary instead of applying a given dictionary for better image representation. Second, we use the learned dictionary with the ℓ1 norm to regularize images. In addition, we apply a total anisotropic variation to enhance the edges of the image. Usually, we use the alternating direction method of multipliers (ADMM) formulation in the Fourier domain for the dictionary. We demonstrate the proposed training scheme for simultaneous image deblurring and inpainting, achieving state-of-the-art results.