Blind Image Deconvolution Algorithm Based on Sparse Optimization with an Adaptive Blur Kernel Estimation

Songmao Chen,Haoyuan Yang,Xiuqin Su

doi:10.3390/app10072437

Abstract

Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly. Therefore, it is essential to develop methods for recovering approximated latent images from blurry ones to increase the performance of the imaging system. In this paper, an image blur removal technique based on sparse optimization is proposed. Most existing methods use different image priors to estimate the blur kernel but are unable to fully exploit local image information. The proposed method adopts an image prior based on nonzero measurement in the image gradient domain and introduces an analytical solution, which converges quickly without additional searching iterations during the optimization. First, a blur kernel is accurately estimated from a single input image with an alternating scheme and a half-quadratic optimization algorithm. Subsequently, the latent sharp image is revealed by a non-blind deconvolution algorithm with the hyper-Laplacian distribution-based priors. Additionally, we analyze and discuss its solutions for different prior parameters. According to the tests we conducted, our method outperforms similar methods and could be suitable for dealing with image blurs in real-life applications.

Highlights

Image deblurring is important in many fields, such as surveillance, traffic control, astronomy, and remote sensing [1,2,3]
Its fundamental principle is expressed as follows: f, k = arg min k k ⊗ f − g k22 +α1 Pf + α2 Pk f,k where the first term, k k ⊗ f − g k22, is the data fidelity term which indicates the prior of the additive noise η in Equation (1), Pf and Pk represent the priors of the original image and the blur kernel, respectively, while α1 and α2 are their corresponding weights
We proposed and evaluated an image deblurring method based on sparse representation

Summary

Introduction

Image deblurring is important in many fields, such as surveillance, traffic control, astronomy, and remote sensing [1,2,3]. Blurs occur due to a variety of reasons, such as moving objects, focus issues, and atmospheric turbulence. They significantly deteriorate the quality of the image. The blurring procedure is always described by a point spread function (PSF), known as blur kernel. When the PSF is known, the blur can be removed by conventional deconvolution methods, such as Weiner filtering and the Lucy–Richardson algorithm. When the PSF is unknown, the issue constitutes a blind-deconvolution problem, which is a notoriously vague inverse problem that has perplexed the scientific community for decades

Methods

Results

Conclusion