Infrared image deblurring with Framelet regularization in the background of salt-and-pepper noise

Abstract

Infrared images have been widely employed in many fields. However, the relative motion between a target and an imaging device and the interference of salt-and-pepper noise lead to the degradation of infrared images. Traditional infrared image recovery methods require an image to be restored as a whole, which leads to mutual interference between the cartoon and texture parts of an image in the restoration process. In this study, a morphological component analysis decomposition method was developed. This method decomposes an image into cartoon and texture parts. Meanwhile, the l p pseudo-norm is introduced to characterize the sparsity of the stationary Framelet transform coefficients and noise, and mask matrix is applied to protect parts that were not contaminated. The proposed method was then solved using an alternating-direction multiplier method. The proposed method was compared with more advanced image recovery methods. The results show that the proposed method has a significant performance improvement.