A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements. © 2020 Elsevier Inc.