Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing

Fast single image defogging with robust sky detection

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.This work was supported in part by the Centro en Investigaciones en Óptica (CIO) and the Consejo Nacional de Ciencia y Tecnología (CONACYT), and in part by the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version

Gamma-correction-based visibility restoration for single hazy images

© 1994-2012 IEEE. In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance

A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect

Real-time image dehazing by superpixels segmentation and guidance filter

Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems