Dehazing Effects on Image and Videousing AHE, CLAHE and Dark Channel Prior
Mahesh Manik Kumbhar1, Bhalchandra B. Godbole2

1Mahesh Manik Kumbhar*, Department of Electronics and Telecommunication, RIRD, Satara, Shivaji University, Kolhapur, Maharashtra, India.
2Dr. Bhalchandra B. Godbole, Department of Electronics Engineering, KBP, Satara, Maharashtra, India.

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 119-125 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C4833029320/2020©BEIESP | DOI: 10.35940/ijeat.C4833.029320
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Abstract: The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outdoor that the value of intensity inside the local window is nearly equal to zero. The DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done using following four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This solution of four step DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc.
Keywords: Computer vision, image processing, image restoration, image enhancement, dehazing, histogram equalization, dark channel prior.