Enhancing Dehazing Performance of Single Optical Satellite Images using Gamma Correction and Improved DCP

Dehazing is an essential preprocessing step for improving the quality of satellite imagery. Hazy conditions can significantly impact the visibility and clarity of satellite images, making it difficult to extract useful information from them. In recent years, several dehazing techniques have been proposed for satellite imagery, including contrast stretching, dark channel prior, and adaptive gamma correction. In this research work, we propose a novel haze removal approach that combines gamma correction with modified dark channel prior-based haze removal approach in the YCbCr color space. Our approach enhances the luminance component of the image by applying gamma correction to the Y channel, and reduces computational complexity by subsampling and combining the chrominance channels. The performance of the proposed system is assessed using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and compared with the state-of-the-art methods. Experimental results demonstrate that the proposed our approach outperforms other methods in terms of dehazing and contrast improvement, suggesting its potential for various applications that require high-quality satellite image processing.