A novel gradient attenuation Richardson–Lucy algorithm for image motion deblurring

Abstract

This paper presents a novel blind image deconvolution algorithm for motion deblurring from a single blurred image. We propose a unified framework for both blur kernel estimation and non-blind image deconvolution by using bilateral filtering (BF) and a new image deconvolution algorithm, called the Gradient Attenuation Richardson–Lucy (GARL) algorithm. In the blur kernel estimation stage, we show that an initial blur kernel, which is used for starting an alternating kernel refinement process, can be obtained from the blurred image with a quadratic regularization approach. In the non-blind image deconvolution stage, we exploit the image gradients and develop the GARL algorithm to alleviate the notorious ringing problem in the Richardson–Lucy-based image restoration approach. Furthermore, the loss of image details due to the suppression of the ringing artifacts around the regions with strong edges is recovered with an incremental detail recovery procedure. The proposed framework is simple yet effective compared to previous statistical approaches. Experimental results on various real data sets are given to demonstrate superior performance of the proposed algorithm over the previous methods.