Image Defogging based on combined Sparse Gradient Minimization and CNN Architecture

Abstract

Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remote sensing which needs clear visible images for making critical decisions. Therefore, restoring the true image from the foggy image becomes significant. Now with the advancement in image processing, many image defogging and dehazing algorithms has been developed to improve the quality of the image. Many standard filtering techniques such as high boost filter, homomorphic filter can be used for image defogging but it fails to restore the foggy images completely so some advanced techniques like dark channel prior, decomposition techniques, convolution network-based algorithms are used. Image quality assessment (IQA) is done to measure the quality of the defogged image. These performance metrics mainly includes mean squared error (MSE), structural similarity index metric (SSIM),peak signal to noise ratio (PSNR).