9,300 research outputs found
Advanced Underwater Image Restoration in Complex Illumination Conditions
Underwater image restoration has been a challenging problem for decades since
the advent of underwater photography. Most solutions focus on shallow water
scenarios, where the scene is uniformly illuminated by the sunlight. However,
the vast majority of uncharted underwater terrain is located beyond 200 meters
depth where natural light is scarce and artificial illumination is needed. In
such cases, light sources co-moving with the camera, dynamically change the
scene appearance, which make shallow water restoration methods inadequate. In
particular for multi-light source systems (composed of dozens of LEDs
nowadays), calibrating each light is time-consuming, error-prone and tedious,
and we observe that only the integrated illumination within the viewing volume
of the camera is critical, rather than the individual light sources. The key
idea of this paper is therefore to exploit the appearance changes of objects or
the seafloor, when traversing the viewing frustum of the camera. Through new
constraints assuming Lambertian surfaces, corresponding image pixels constrain
the light field in front of the camera, and for each voxel a signal factor and
a backscatter value are stored in a volumetric grid that can be used for very
efficient image restoration of camera-light platforms, which facilitates
consistently texturing large 3D models and maps that would otherwise be
dominated by lighting and medium artifacts. To validate the effectiveness of
our approach, we conducted extensive experiments on simulated and real-world
datasets. The results of these experiments demonstrate the robustness of our
approach in restoring the true albedo of objects, while mitigating the
influence of lighting and medium effects. Furthermore, we demonstrate our
approach can be readily extended to other scenarios, including in-air imaging
with artificial illumination or other similar cases
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
PRIDNet based Image Denoising for Underwater Images
Underwater image enhancement has become a popular research topic due to its importance in aquatic robotics and marine engineering. However, the underwater images frequently experience signal-dependent speckle noise when transmitting and acquiring data, which can limit certain applications such as detection, object tracking. In the recent years, the existing underwater image enhancement algorithms efficiency has been analysed and evaluated on a small number of carefully chosen real-world images or synthetic datasets. As such, it is challenging to predict how these algorithms might function with images acquired in the wild under various circumstances. This paper introduces a new solution for noise removal from underwater images called Pyramid Real Image Noise Removal Network (PRIDNet) with patches.PRIDNet is a three-level network design using image patches. The tests were carried out on a dataset of actual noisy images demonstrate that, in terms of quantitative metrics, our proposed denoising model reduction performs better with the exixting denoisers. We determine the effectiveness and constraints of existing algorithms using benchmark assessments and the suggested model, offering valuable information for further studies on underwater image enhancement
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
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