Handling Outliers in Non-Blind Image Deconvolution

Abstract

Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.