1,454 research outputs found
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
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Robust vision restoration for an underwater image remains a challenging
problem. For the lack of aligned underwater-terrestrial image pairs, the
unsupervised method is more suited to this task. However, the pure data-driven
unsupervised method usually has difficulty in achieving realistic color
correction for lack of optical constraint. In this paper, we propose a data-
and physics-driven unsupervised architecture that learns underwater vision
restoration from unpaired underwater-terrestrial images. For sufficient domain
transformation and detail preservation, the underwater degeneration needs to be
explicitly constructed based on the optically unambiguous physics law. Thus, we
employ the Jaffe-McGlamery degradation theory to design the generation models,
and use neural networks to describe the process of underwater degradation.
Furthermore, to overcome the problem of invalid gradient when optimizing the
hybrid physical-neural model, we fully investigate the intrinsic correlation
between the scene depth and the degradation factors for the backscattering
estimation, to improve the restoration performance through physical
constraints. Our experimental results show that the proposed method is able to
perform high-quality restoration for unconstrained underwater images without
any supervision. On multiple benchmarks, we outperform several state-of-the-art
supervised and unsupervised approaches. We also demonstrate that our methods
yield encouraging results on real-world applications
Underwater Turbid Media Stokes-Based Polarimetric Recovery
Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and
designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions
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