A Survey on Blind De-Blurring of Digital Image (original) (raw)

A Comparative Study On Image Deblurring Techniques

2014

Image blur is a common problem that occurs when recording digital images due to camera shake, long exposure time, or movement of objects. As a result, the recorded image is degraded and the recorded scene becomes unreadable. Recently, the field of blur removal has gained increasing interest in a lot of researches. The problem is known as blind deconvolution if the only available information is the blurred image and there is no knowledge about the blurring model or the Point Spread Function (PSF). In this case, the basic target of the process is to recover both the blur kernel and the deblurred (latent) image, simultaneously. In this paper, we introduced a comprehensive study on the image deblurring, type of blur, noise model and finally a comparative study of different image deblurring techniques. We performed several experiments to evaluate these techniques in terms of performance, blur type, Peak Signal to Noise Ratio and structural similarity (SSIM).

A Survey on Blurred Images with Restoration and Transformation Techniques

International Journal of Computer Applications, 2013

In modern science and technology, digital images gaining popularity due to increasing requirement in many fields like medical research, astronomy, remote sensing, graphical use etc. Therefore, the quality of images matters in such fields. There are many ways by which the quality of images can be improved. Image restoration is one of the emerging methodologies among various existing techniques. Image restoration is the process of simply obtaining an estimated original image from the blurred, degraded or corrupted image. The primary goal of the image restoration is the original image is recovered from degraded or blurred image .This paper contains the review of many different schemes of image restoration that are based on blind and nonblind de-convolution algorithm using transformation techniques.

A Method for Blind Deblurring of Natural Images

Image de blurring is an inverse problem whose aim to recover an image from a version of that image which has suffered a linear degradation and noise. A method for blind image de blurring is presented. The method only makes weak assumptions about the blurring filter and is able to undo wide variety of de blurring degradations .But effective ness of this approach under presence of noise is still confused. To overcome this an approach is investigated where, in presence of asymmetric noise, a preprocessing of image, i.e. denoising the image. To denoising the image we used two techniques, bilateral filtering and singular value decomposition de noising. We comparing the performance using these two methods to restore the image. To overcome the ill-posedness of the blind image de blurring problem, the method includes a learning technique which initially focuses on the main edges of the image and gradually takes details into account. A new image prior, which includes a new edge detector, is used. The method is able to handle unconstrained blurs, but also allows the use of constraints or of prior information on the blurring filter, as well as the use of filters defined in a parametric manner. Furthermore, it works in both single-frame and multiform scenarios. The use of constrained blur models appropriate to the problem at hand, and/or of multiform scenarios, generally improves the de blurring results. Tests performed on monochrome and colour images.

Blurred Image Enhancement Using Contrast Stretching , Local Edge Detection and Blind Deconvolution

2014

Blurring of image is common problem while taking picture of an object in motion or due to shooting situations. Various methods have been proposed to enhance the blurred image. Here contrast stretching is used for obtaining deblurred image. In the proposed method local edge detection is applied on original as well as contrast stretched image. The set of edges obtained from both the images are fussed in order to get sharper edges. The original image and contrast stretched image is converted into gray scale image from RGB image before applying local edge detection to avoid detection of false edges. Since image distortion information is unknown, so on the obtained fussed image blind deconvolution is applied to get deblurred image.

A Novel Method for Removing Gaussian Blur from Images

2018

The aim of this paper is to develop a methodology in order to remove the blur from an image at a larger extent. One of the main problems in this research field is the quality of an images. So in this paper is propose an algorithm for improving the quality of an image by removing Gaussian blur, which is an image blur. The deblurring techniques are basically used to sharp an image using different methods & parameters. The deblurring techniques which is used in this research paper is basic derivative method and derivatives which are first series, second, third and fourth series respectively to recover a

Comparative Analysis of Different Deblurring Techniques

International journal of engineering research and technology, 2018

Cameras shake during exposure time results in unpleasant image blur and is reason of rejection of many photographs. Typical blind deconvolution methods assume frequency-domain constraints on images, or extremely basic parametric forms for the motion trail during camera shake. Actual camera motion trajectory can follow convoluted path, and a spatial domain prior can preserve prominent image characteristics. A blurred image can be recognized as a convolution function of a sharp image and a blur kernel or PSF. So in order to recover the sharp image we need to split the image into its blur kernel and sharp image. The problem lies with the blur kernel estimation. Few methods assume a uniform camera blur over the image and negligible in-plane camera rotation. This unknown blur kernel estimation is called as the Blind deconvolution. Most of the deblurring techniques make use of these concepts. Few months uses sensors to calculate this blur Kernel. Paper aims reviewing all the contemporary ...

Single image blind deblurring with image decomposition

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012

How to deal with the motion blurred image is a common problem in our daily life. Restoring blurred images is challenging, especially when both the blur kernel and the sharp image are unknown. In this work, we present a new algorithm for removing motion blur from a single image, which incorporates the image decomposition into the image deblurring process. Most of the existing algorithms solving the blind deblurring problem use the alternate iterative mechanism, which alternative estimates the kernel and restores the sharp image. We find that the small gradients of image are not always helpful but sometimes harmful to this kind of iterative algorithm. So we decompose the blurred image into cartoon and texture components. And we only use the cartoon part of the image, which can improve the stability and robustness of the algorithm. Our experiments show that our algorithms can achieve good results in man-made and real-life photos.

Review of Techniques and Methods for Image Deblurring

United International Journal for Research & Technology, 2020

Technological advancements in the scientific world has witnessed its exponential growth since the transformation from analogue to digital devices. Computer Systems transitioned from humongous sized poor performance gadgets to powerful computational devices even surpassing the human intelligence in most scenarios. Such advancements gave birth to algorithmic approach to world most complex problems. As algorithms, for their decision making requires data, which in most cases is in the form of images, those images for the sake for better processing needs to be clear with sharp edges, which simply means there shouldn't be any kind of blur or noise effecting those input images. Blur or noise are added to the input images during the capturing process either because of the natural scene lighting or the complexity of scene or because of convolution of impulse response which is called as blur kernel or point spread function(PSF). Image processing field which deals with deblurring of such images is called as image restoration. There are two methodologies for dealing with such scenarios that are blind image deblurring and reference based image deblurring. This paper gives extensive review of research done in both domains. Discussing in details the approaches been used along with comparing the data to analyze and specify the methodology that is well suited for most blur scenarios.

A Novel Approach for Shaken Image Deblurring

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014

Blurry images are the bane of many photographers. Although sometimes these images could be retaken in the hope that the next exposure will not be blurred, but frequently they are of some unique event that could only be captured once. The most common cause of blurry images is camera shake. Camera shake means that during the exposure the camera moved. This movement may be very small but still creates blurry images. In this paper, a novel blinddeblurring approach for removing the effect of camera shake from blurry image is proposed. Starting with an image that has been blurred by camera shake, we recover the unknown shape image in two phases: (i) a kernel estimation phase using Radon transform method, and (ii) the shape image recovery based on EM algorithm. Comprehensive comparisons on a number of blurry images show that our approach is not only substantially faster, but it also leads to better deblurring results. Our experimental results are also shown for comparisons. Visually, we find that the restored images are better than those given by the algorithm in other methods from previous works.