Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods
G Harish Babu1, N Venkatram2, M Kavya3
1G Harish Babu*, Department of Electronics and Communication Engineering, CVR College of Engineering, Hyderabad, India.
2M Kavya, Department of Electronics and Communication Engineering, CVR College of Engineering, Hyderabad, India.
3N Venkatram, Dean Academics, Koneru Lakshmaiah Educational Foundation, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 23 September, 2019. | Manuscript published on October 10, 2019. | PP: 3015-3020 | Volume-8 Issue-12, October 2019. | Retrieval Number: K23370981119/2019©BEIESP | DOI: 10.35940/ijitee.k2337.1081219
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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: Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.
Keywords: Dark Channel Prior, Dehazing
Scope of the Article: Wireless Power Transmission