Blind color image deconvolution based on wavelet decomposition

Yu He, Kim-Hui Yap, Li Chen, Lap-Pui Chau

Abstract: This paper presents a new framework to address blind color image deconvolution based on wavelet decomposition. Blind color image deconvolution is a challenging problem due to the lack of information available. Conventional methods based on single-input single-output (SISO) model experience significant color artifacts in the restored images. On the other hand, there are limitations on the practicality of using multi-input multi-output (MIMO) model in solving this problem as the color channels are usually highly correlated. In view of this, the paper proposes a new framework to solve blind color image deconvolution by first decomposing the color channels into wavelet subbands, and performing image deconvolution using a combination of SISO and single-input multi-output (SIMO) models. Experimental results show that the proposed method is able to achieve satisfactory restored images.