Accurate single-image defocus deblurring based on improved integration with defocus map estimation

This paper considers the problem of single-image defocus deblurring, which involves removing blur in an input image caused by defocusing. Previous studies have employed two main approaches, the first being a two-step approach involving estimating the defocus map from the input image and then computing the blur kernel from it, followed by non-blind deconvolution to obtain the estimate of the clean image. The second approach is a direct method where the clean image is estimated directly from the blurry input image. The paper proposes an intermediate approach that explicitly estimates the defocus map of the scene but does not explicitly compute the kernel or its inverse. Instead, it attempts to learn a direct mapping from the blurry input image to the clean image by utilizing the estimated defocus map to condition the mapping. Experimental results show that the proposed method can yield higher quality outputs than the state-of-the-art methods.