A Single Image Deblurring Algorithm for Nonuniform Motion Blur Using Uniform Defocus Map Estimation

Abstract

One of the most common artifacts in digital photography is motion blur. When capturing an image under dim light by using a handheld camera, the tendency of the photographer’s hand to shake causes the image to blur. In response to this problem, image deblurring has become an active topic in computational photography and image processing in recent years. From the view of signal processing, image deblurring can be reduced to a deconvolution problem if the kernel function of the motion blur is assumed to be shift invariant. However, the kernel function is not always shift invariant in real cases; for example, in-plane rotation of a camera or a moving object can blur different parts of an image according to different kernel functions. An image that is degraded by multiple blur kernels is called a nonuniform blur image. In this paper, we propose a novel single image deblurring algorithm for nonuniform motion blur images that is blurred by moving object. First, a proposed uniform defocus map method is presented for measurement of the amounts and directions of motion blur. These blurred regions are then used to estimate point spread functions simultaneously. Finally, a fast deconvolution algorithm is used to restore the nonuniform blur image. We expect that the proposed method can achieve satisfactory deblurring of a single nonuniform blur image.

No-Reference Digital Image Quality Assessment Based on Structure Similarity

“…where g is the blurry image, H indicates the distortion facto, known as the point spread function (PSF), n is added noise, and * represents the convolution operator [8]. In the spatial field, the PSF characterizes the rate at which the optical system blurs the spotlight [1][10], Figure1 illustrates image deblurring model. In the proposed algorithm a blurred image is obtained by convolving the original image and blurring kernel (PSF).…”

“…In the proposed algorithm a blurred image is obtained by convolving the original image and blurring kernel (PSF). The PSF parameters (angle and length) can be calculated by first estimating the angle quite accurately using analysis in the Cepstrum domain [12], for a given angle we can estimate the length of the blur in the image for a given angle [1]. The Winner filter is applied to the blurred image to get the true image as the equation described below:…”

“…1:Input pristine image 2: Determine the blurred image by convolving the original image and blurring kernel (PSF) using equation (1) The PSF parameters (angle and length) can be calculated using analysis in the Cepstrum domain The image deblurred with a blur kernel similar to the true PSF shown in is the one that will reproduce a similar blur when reblurred.…”

“…Image quality assessment (IQA) is important for numerous image processing measures, such as re-extraction, restoration, enhancement, compression, and acquisition [1]. IQA is divided into Non-reference NR IQA that's refer to the automatic evaluation of image quality using an algorithm so that the only information the algorithm receives before it predicts quality is the distorted image whose quality is obtained, Full-reference FR IQA that requires as input not only the distorted image, but also pristine image which the distorted image quality is evaluated, reduced reference (RR) approaches that possess some information regarding the reference image, but not the actual reference image itself, regardless of the distorted image [11].…”

A Survey on Blind De-Blurring of Digital Image

“…Finally, a quick deconvolution algorithm is employed to restore the nonuniform blur image. this method is effective for any motion blur, but it is shadows tend to cause the algorithm to detect blurred objects incorrectly [24].…”