72 research outputs found
Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
This paper presents a comprehensive survey of low-light image and video
enhancement. We begin with the challenging mixed over-/under-exposed images,
which are under-performed by existing methods. To this end, we propose two
variants of the SICE dataset named SICE_Grad and SICE_Mix. Next, we introduce
Night Wenzhou, a large-scale, high-resolution video dataset, to address the
issue of the lack of a low-light video dataset that discount the use of
low-light image enhancement (LLIE) to videos. Our Night Wenzhou dataset is
challenging since it consists of fast-moving aerial scenes and streetscapes
with varying illuminations and degradation. We conduct extensive key technique
analysis and experimental comparisons for representative LLIE approaches using
these newly proposed datasets and the current benchmark datasets. Finally, we
address unresolved issues and propose future research topics for the LLIE
community. Our datasets are available at
https://github.com/ShenZheng2000/LLIE_Survey.Comment: 13 pages, 8 tables, and 13 figure
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks
The limited dynamic range of commercial compact camera sensors results in an
inaccurate representation of scenes with varying illumination conditions,
adversely affecting image quality and subsequently limiting the performance of
underlying image processing algorithms. Current state-of-the-art (SoTA)
convolutional neural networks (CNN) are developed as post-processing techniques
to independently recover under-/over-exposed images. However, when applied to
images containing real-world degradations such as glare, high-beam, color
bleeding with varying noise intensity, these algorithms amplify the
degradations, further degrading image quality. We propose a lightweight
two-stage image enhancement algorithm sequentially balancing illumination and
noise removal using frequency priors for structural guidance to overcome these
limitations. Furthermore, to ensure realistic image quality, we leverage the
relationship between frequency and spatial domain properties of an image and
propose a Fourier spectrum-based adversarial framework (AFNet) for consistent
image enhancement under varying illumination conditions. While current
formulations of image enhancement are envisioned as post-processing techniques,
we examine if such an algorithm could be extended to integrate the
functionality of the Image Signal Processing (ISP) pipeline within the camera
sensor benefiting from RAW sensor data and lightweight CNN architecture. Based
on quantitative and qualitative evaluations, we also examine the practicality
and effects of image enhancement techniques on the performance of common
perception tasks such as object detection and semantic segmentation in varying
illumination conditions.Comment: Accepted in BMVC 202
Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey
Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research
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