Quality Improvements of Camera Captured Pictures using Blind and Non-blind Deconvolution Algorithms
Pallavi U. Patil1, Sudhir B. Lande2, Vinay J. Nagalkar3, Sonal B. Nikam4
1Pallavi U. Patil*, Department of Electronics and Telecommunication, VPKBIET, Baramati, Pune, India.
2Dr. Sudhir B. Lande, Department of Electronics and Telecommunication, VPKBIET, Baramati, Pune, India.
3Dr. Vinay J. Nagalkar, Department of Electronics and Telecommunication, VPKBIET, Baramati, Pune, India.
4Sonal B. Nikam, Department of Electronics and Telecommunication, VPKBIET, Baramati, Pune, India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 73-78 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4788119420 | DOI: 10.35940/ijrte.D4788.119420
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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: Camera captured image is a set of three-dimensional picture frame. This picture frame is a set of different characteristics and parameters. Captured picture suffers from image blurring parameters. These blurring parameters are created by camera misfocus, motion, atmospheric causes, camera sensor noise etc. Thus, captured picture is represents the blurry image format due to lot of interferences occurs in the surrounding background and picture captured device. Hence, some information is corrupted i.e. degradation occurs in the camera captured picture. Therefore, it needs to reconstruct the original picture using image restoration process. Restoration operation includes different image deblurring algorithms such as Non-blind deconvolution and Blind deconvolution algorithms. Non-blind deconvolution algorithms are more effective when blurring parameters of captured picture is known but Blind deconvolution algorithm recover the blurry image without prior knowledge about blurring parameters.
Keywords: Additive Noise, Blur Detection, Blurred Operator, Convolution, Deconvolution, Image Restoration.