A Novel Hybrid Model Framework to Blind Color Image Deconvolution

This paper presents a new hybrid model framework to address blind color image deconvolution. Blind color image deconvolution is a challenging problem due to the limited information on the blurring function. Conventional methods based on the single-input single-output (SISO) model experience suboptimal results as each color channel is processed independently. On the other hand, there are limitations on the practicality of using a multiinput multioutput (MIMO) model in solving this problem as the color channels are usually highly correlated. In view of these constraints, this paper proposes a novel framework to solve blind color image deconvolution by first decomposing the color channels into wavelet subbands, and performing image deconvolution using a hybrid of SISO and single-input multioutput models. The proposed method utilizes the correlation information among different color channels to alleviate the constraints imposed by the MIMO systems. Experimental results show that the method is able to achieve satisfactory restored images under different noise and blurring environments.