Image De-Blurring Based on Constraint Conditional Model
Ravikumar H C1, P Karthik2
1Ravikumar H C*, Research scholar, Dept. of ECE, KSSEM, Bengaluru, & Asst. Prof., Dept.of ECE, Ghousia College of Engg, Ramanagaram , India
2P Karthik, Professor, Dept. of ECE, KSSEM, Bengaluru, India
Manuscript received on December 18, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 995-1000 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8525019320/2020©BEIESP | DOI: 10.35940/ijitee.C8525.019320
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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: Image capturing is more vulnerable to the various physical limitations such as defocus, low lighting and camera shaking; this makes the image blurry and noisy. Moreover De-blurring is the process to recover the original image from the given degraded image. De-blurring technique uses the estimated blur kernel for achieving the optimal restored image with the sharp features, however the accuracy has been one of the major concern , hence in this paper we use Constrained Conditional model (CCM) for restoring the image. Moreover, here two different methods are integrated i.e. conditional model and convergence operator, these two combined learns the model and efficiently and provides the better results. In order to evaluate the proposed model, Levin dataset is used by considering the two eminent model metric i.e. PSNR and SSIM and CCM based model outperforms the other state-of-art technique.
Keywords: De-blur, CCM, Image Restoration
Scope of the Article: Image analysis and processing