Robust Image Restoration for Motion Blur of Image Sensors.

Fasheng Yang,Lixing Li,Yongmei Huang,Yihan Luo,Hongwei Li

doi:10.3390/s16060845

Abstract

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively.

Highlights

IntroductionMotion blur widely exists in digital photography photography and leads to disappointing disappointing blurry images with sensors that integrate incoming lights for inevitable information information loss. loss
In order to eliminate the effect of noise and ambiguous structures which may damage kernel estimation, we propose a novel structure extraction method with which the reliable structures can be selected adaptively and effectively; As motion blur kernel is sparse and delineates the motion trace between the subject and image sensors, we introduce a two-step method for the kernel estimation process to eliminate the noise and guarantee the sparsity and continuity
We propose a robust image restoration algorithm for motion blur of image sensors

Summary

Introduction

IntroductionMotion blur widely exists in digital photography photography and leads to disappointing disappointing blurry images with sensors that integrate incoming lights for inevitable information information loss. loss. Motion blur widely exists in digital photography photography and leads to disappointing disappointing blurry images with sensors that integrate incoming lights for inevitable information information loss. Due. Duetotothe themechanism mechanismofofimage image sensors that integrate incoming lights an amount of time to produce images, if a relative motion happens between the subject and the image for an amount of time to produce images, if a relative motion happens between the subject and the sensorssensors duringduring the integration time, atime, blurred image will bewill produced as shown in Figure image the integration a blurred image be produced as shown in Figure Figure Figure 1. Image degradation degradation model model for for motion motion blur blur of of image image sensor.

Methods

Results

Conclusion