LAUSR

This paper proposes a novel deep learning-based single image dehazing network named as Compact Single Image Dehazing Network (CSIDNet) for outdoor scene enhancement. CSIDNet directly outputs a haze-free image from the given hazy input. The remarkable features of CSIDNet are that it has been designed only with three convolutional layers and it requires lesser number of images for training without diminishing the performance in comparison to the other commonly observed deep learning-based dehazing models. The performance of CSIDNet has been analyzed on natural hazy scene images and REalistic Single Image DEhazing (RESIDE) dataset. RESIDE dataset consists of Outdoor Training Set (OTS), Synthetic Objective Testing Set (SOTS), and real-world synthetic hazy images from Hybrid Subjective Testing Set (HSTS). The performance metrics used for comparison are Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) index. The experimental results obtained using CSIDNet outperform several well known state-of-the-art dehazing methods in terms of PSNR and SSIM on images of SOTS and HSTS from RESIDE dataset. Additionally, the visual comparison shows that the dehazed images obtained using CSIDNet are more appealing with better edge preservation. Since the proposed network requires minimal resources and is faster to train along with lesser run-time, it is more practical and feasible for real-time applications.