Deep Single Image Defocus Deblurring via Gaussian Kernel Mixture Learning
Yuhui Quan, Zicong Wu, Ruotao Xu, Hui Ji
Abstract
This paper proposes an end-to-end deep learning approach for removing defocus blur from a single defocused image. Defocus blur is a common issue in digital photography that poses a challenge due to its spatially-varying and large blurring effect. The proposed approach addresses this challenge by employing a pixel-wise Gaussian kernel mixture (GKM) model to accurately yet compactly parameterize spatially-varying defocus point spread functions (PSFs), which is motivated by the isotropy in defocus PSFs. We further propose a grouped GKM (GGKM) model that decouples the coefficients in GKM, so as to improve the modeling accuracy with an economic manner. Afterward, a deep neural network called GGKMNet is then developed by unrolling a fixed-point iteration process of GGKM-based image deblurring, which avoids the efficiency issues in existing unrolling DNNs. Using a lightweight scale-recurrent architecture with a coarse-to-fine estimation scheme to predict the coefficients in GGKM, the GGKMNet can efficiently recover an all-in-focus image from a defocused one. Such advantages are demonstrated with extensive experiments on five benchmark datasets, where the GGKMNet outperforms existing defocus deblurring methods in restoration quality, as well as showing advantages in terms of model complexity and computational efficiency.