FR-HDNet: Faster RCNN based Haze Detection Network for Image Dehazing

Abstract

Image dehazing plays a major role in several vision- based applications aiming to improve image quality to obtain rich textural information. This paper proposes a methodology to retain textural information after image enhancement for vision- based algorithms. The objective is first to detect the hazy regions in a hazy input image and then perform dehazing on these detected regions. This results in retaining the texture of haze- free regions and an enhanced view of hazy regions. In the first part of the proposed framework, a Faster RCNN-based haze detection network named FR-HDNet is proposed to identify the hazy regions in an input hazy image. In the second part, the detected hazy regions are dehazed. This results in an enhanced image optimally equipped with features that could aid vision- based algorithms. Finally, the enhanced dehazed image is fed into an object detection network. The experiments to validate the performance of the proposed framework are done on several benchmarked datasets like natural hazy benchmarked images frequently used in the literature, synthetic hazy images, indoor Synthetic Objective Testing Set (SOTS) images from REalistic Single Image DEhazing (RESIDE) dataset, outdoor SOTS images from RESIDE dataset, and real-world synthetic hazy images of Hybrid Subjective Testing Set (HSTS) from RESIDE dataset. The performance measures used to evaluate the quality of dehazed images are Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) index; and Lightness Order Error (LOE) and Naturalness Image Quality Evaluator (NIQE) as no- reference image quality metrics. The effectiveness of the proposed framework is compared with several benchmarked state-of-the- art dehazing methods. The comparison demonstrated that the proposed framework enhances image quality and results in better performance.