A Generalized Additive Convolution Model for Efficient Deblurring of Camera Shaken Image

Hong Deng, Dongwei Ren, Hongzhi Zhang, Kuanquan Wang, Wangmeng Zuo

Abstract: Image blur caused by camera shake is often spatially variant, which makes it more challenging to recover the latent sharp image. Geometrical camera shake model based non-uniform deblurring methods, modeling the blurry image as a weighted summation of the homographically transformed images of the latent sharp image, although can achieve satisfactory deblurring results, still suffer from the problems of heavy computation or extensive memory cost. In this paper, we propose a generalized additive convolution (GAC) model for efficient non-uniform deblurring. A camera motion trajectory can be decomposed into a compact set of in-plane translations (slice) and roll rotations (fiber), which with an insightful analysis can both be formulated as convolution. The GAC model provides a promising way to overcome the difficulties of both computational load and memory burden. The experimental results show that GAC can obtain satisfactory deblurring results, and is much more efficient than state-of-the-arts.