Dehazing of Images using Minimum White Balance Optimization
Deoyani Mujbaile1, Dinesh Rojatkar2
1Deoyani Mujbaile, Department of Electronics Engineering, Government College of Engineering Amravati, Amravati, Maharashtra, India.
2Dinesh Rojatkar, Department of Electronics Engineering, Government College of Engineering Amravati, Amravati, Maharashtra, India.
Manuscript received on May 04, 2020. | Revised Manuscript received on May 20, 2020. | Manuscript published on June 10, 2020. | PP: 13-19 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.G5873059720 | DOI: 10.35940/ijitee.G5873.069820
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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: The quality of image captured in presence of fog and haze is degraded due to atmospheric scattering. In order to restore such images, several dehazing algorithms have been proposed. These algorithms sometimes, results in either a contrast distorted dehazed image or a dehazed image that has influence of dense haze. In order to solve this problem, dynamic facsimile dehaze system built on minimum white balance optimization is proposed. This paper proposed a system that integrates some famous single image dehazing algorithms and enhance their outputs using histograms and adaptive histograms; then adaptively select the output with minimum white balance distortion in order to get the optimum output. Experimental results demonstrated that the presented system can attain better dehazing effect and further improves universality of dehazing methods. Also proposed system improves luminance and contrast of dehazed images to a certain extent.
Keywords: Adaptive histogram equalization, Contrast distorted, Dynamic, Histogram equalization.
Scope of the Article: Discrete Optimization