Single Image Haze Removal using Contextual Regularization
C. Shobana Nageswari1, M.N.Vimal Kumar2, K.Ilamathi3, R. Mamtha4
1C. Shobana Nageswari*, Department of ECE, R.M.D Engineering College, Anna University, Chennai, India.
2M.N.Vimal Kumar, Department of ECE, R.M.D Engineering College, Anna University, Chennai, India.
3K.Ilamathi, Department of ECE, R.M.D Engineering College, Anna University, Chennai, India, Email
4R. Mamtha, Systems Engineer, Infosys, Bangalore, India.
Manuscript received on January 20, 2020. | Revised Manuscript received on January 29, 2020. | Manuscript published on February 10, 2020. | PP: 2929-2933 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1926029420/2020©BEIESP | DOI: 10.35940/ijitee.D1318.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Haze is a condition where our visibility gets affected due to particles, smoke, dust and moisture which are suspended in air. Haze reduces the visibility. A contextual regularization de-hazing algorithm is proposed which uses a single frame image for enhancing the foggy image using multilevel transmission mapping. Quantitative parameters such as MSE and PSNR are considered to assess the superiority of the proposed method. The proposed method is fast and free from noise when compared with other existing methods.
Keywords: Haze Removal, Contextual Regularization, MSE, PSNR
Scope of the Article: Image Security