Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Images captured under poor illumination conditions often exhibit characteristics such as low
brightness, low contrast, a narrow gray range, and color distortion, as well as considerable …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of
an image captured in an environment with poor illumination. Recent advances in this area …

The real-time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral
RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

Images captured under incorrect exposures unavoidably suffer from mixed degradations of
lightness and structures. Most existing deep learning-based exposure correction methods …

Deep learning-based methods have achieved remarkable success in image restoration and
enhancement, but are they still competitive when there is a lack of paired training data? As …

Low-light images suffer from low contrast and unclear details, which not only reduces the
available information for humans but limits the application of computer vision algorithms …