International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
AN APPROACH FOR IMAGE DEBLURRING: BASED ON SPARSE REPRESENTATION AND REGULARIZED FILTER Ragulganthi M*1, Deepa P2, Gayathri G3, Vinoth R4 1Pg
scholar, Dept of ECE, Government College of Technology, Coimbatore, Tamilnadu, India professor, Dept of ECE, Government College of Technology, Coimbatore, Tamilnadu, India 3Pg scholar, Dept of ECE, Government College of Technology, Coimbatore, Tamilnadu, India 4Pg scholar, Dept of ECE, Government College of Technology, Coimbatore, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Assistant
Abstract - Deblurring of the image is most the fundamental problem in image restoration. The existing methods utilize prior statistics learned from a set of additional images for deblurring. To overcome this issue, an approach for deblurring of an image based on the sparse representation and regularized filter has been proposed. The input image is split into image patches and processed one by one. For each image patch, the sparse coefficient has been estimated and the dictionaries were learned. The estimation and learning were repeated for all patches and finally merge the patches. The merged patches are subtracted from blurred input image the deblur kernel to be obtained. The deblur kernel then applied to regularized filter algorithm the original image to be recovered without blurring. The proposed deblur algorithm has been simulated using MATLAB R2013a (8.1.0.604). The metrics and visual analysis shows that the proposed approach gives better performance compared to existing methods.
computer vision community. Deblurring is the combination of two sub-problems: Point spread function (PSF) estimation and non-blind image deconvolution. These problems are both independently in computer graphics, computer vision, and image processing [13]. Finding a sparse representation of input data in the form of a linear combination of basic elements. It is called sparse dictionary learning and this is learning method. These elements are composing a dictionary. Atoms in the dictionary are not required to be orthogonal [10]. One of the key principles of dictionary learning is that the dictionary has to be inferred from the input data. The sparse dictionary learning method has been stimulated by the signal processing to represent the input data using as few possible components. To unblurred an image the non-blind deconvolution blur Point Spread Function (PSF) has been used [14].The previous works to restore an image based on RichardsonLucy or Weiner filtering have more noise sensitivity[15 16]. Total Variation regularizer heavy-tailed normal image priors and Hyper-Laplacian priors were also widely studied [17].Blind deconvolution can be performing iteratively, whereby each iteration improves the estimation of the PSF [8].
Key Words: Image deblurring, Dictionary learning based image sparse representation, Regularized filter
1.INTRODUCTION Image deblurring is one of the problems in image restoration. The image blurring causes due to camera shake. The image blur can be modelled by a latent image convolving with a kernel K. B = K ⊗I + n,
In [3] found that a new iterative optimization to solve the kernel estimation of images. To deblur images with very large blur kernels is very difficult. to reduce this difficulty using the iterative methods to deblur the image. From [1] found that to solve the kernel estimation and large scale optimization is used unnaturall0 sparse representation [1].The properties for latent text image and the difficulty of applying the properties to text image de-blurring is discussed in [2].Two motion blurred images with different blur directions and its restoration quality is superior than when using only a single image [5].A deblurring methods can be modelled as the observed blurry image as the convolution of a latent image with a blur kernel[6].
(1)
Where B, n and I represent the input blurred image, latent image and noise respectively. The ⊗ denotes convolution operator and the deblurring problem in image are thus posed as deconvolution problem [13]. The process of removing blurring artifacts from images caused by motion blur is called deblurring. The blur is typically modeled as the convolution of a point spread function with a latent input image, where both the latent input image and the point spread function are unknown. Image deblurring has received a lot of attention in
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