Single Image Dehaze using Deep Learning with Beetle Swarm Optimization Algorithm
R.Prakash Kumar1, Manja Naik. N2
1R.Prakash Kumar, Research Scholar, Department of Electronics and Communication Engineering, UBDTCE, VTU, Davangere (Karnataka), India.
2Dr. Manja Naik.N, Professor, Department of Electronics and Communication Engineering, UBDTCE, VTU, Davangere (Karnataka), India.
Manuscript received on 24 January 2024 | Revised Manuscript received on 13 March 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024 | PP: 1-6 | Volume-11 Issue-11, November 2024 | Retrieval Number: 100.1/ijies.C980713030224 | DOI: 10.35940/ijies.C9807.11111124
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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: Particles present in the atmosphere the captured light is deviated into scattering due to this, haze is captured by camera. The process of removal of haze in an image is called De-hazing. Dehazing is a challenging task in computer vision and surveillance applications. Deep learning methods have been developed and shown encouraging results. However, these approaches have significant impact on how well these approach work. In this paper we introduce an innovative deep learning as Beetle Swarm Optimization (BSO) algorithm for single image dehazing. BSO is a nature inspired optimization algorithm that uses beetle’s social behavior as model to get the best response. The dehazing model performs more effectively after the parameters of deep learning network optimized using BSO. The experimental results indicate well how our method works at eliminating haze from single images. Benchmark data sets are used to access the suggested strategy. In this paper the proposed method is evaluated in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSI) also our method produces dehazed images with high contrast and colour accuracy as well as more visually pleasing shots. The proposed method which has applications in surveillance, remote sensing and self-driving cars provides a dependable and efficient solution to dehaze a single image.
Keywords: Dehazing, Atmospheric Light, Convolution Neural Networks.
Scope of the Article: Image Analysis and Processing