Adaptive Deblurring by Estimation of Motion Blur Kernels
Athira S Vijay1, Nelwin Raj N. R2
1Athira S Vijay, Electronics and Communication Engineering Department, Sree Chitra Thirunal College Of Engineering, Pappanamcode, Trivandrum (Kerala), India.
2Nelwin Raj N. R, Electronics and Communication Engineering Department, Sree Chitra Thirunal College Of Engineering, Pappanamcode, Trivandrum (Kerala), India.
Manuscript received on 13 June 2016 | Revised Manuscript received on 20 June 2016 | Manuscript Published on 30 June 2016 | PP: 49-56 | Volume-5 Issue-5, June 2016 | Retrieval Number: E4595065516/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the challenges in the field of photography is the motion blur. Motion blur is the smudging of images caused by the relative motion between the camera and the pictured object during the exposure time. Blur kernel is the fundamental cause for blurring. Thus, in order to restore the original image through deconvolution, we need to estimate the blur kernel. In this paper, the blur kernels are estimated by using a piecewise linear model. Then, estimated kernel is regularized by adjusting the spacing and curvature of the control points. In addition to this, the control parameters of the energy function is also optimized in order to achieve better edge enhancement. The estimated kernel is then optimized by using Gauss- Newton method. In order to improve the PSNR of the deblurred image, wavelet multiframe denoising is used. In addition to this, the quality of image is enhanced by using a colour image enhancement technique. The experimental result shows that, kernel estimation along with wavelet multiframe denoising and Colour image enhancement technique can improve the PSNR values as well as the quality of the resultant deblurred image. In addition to this, the proposed algorithm can accurately estimate the unknown kernel masked in the blurred image, without any prior knowledge.
Keywords: Motion Blur, Piecewise-Linear Curve, Kernel Estimation, Deblurring, Wavelet Multiframe Denoising, PSF, Blind Deconvolution, Image Enhancement.
Scope of the Article: Image Analysis and Processing