708 research outputs found
KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image
Low-light images often suffer from noise and color distortion. Object
detection, semantic segmentation, instance segmentation, and other tasks are
challenging when working with low-light images because of image noise and
chromatic aberration. We also found that the conventional Retinex theory loses
information in adjusting the image for low-light tasks. In response to the
aforementioned problem, this paper proposes an algorithm for low illumination
enhancement. The proposed method, KinD-LCE, uses a light curve estimation
module to enhance the illumination map in the Retinex decomposed image,
improving the overall image brightness. An illumination map and reflection map
fusion module were also proposed to restore the image details and reduce detail
loss. Additionally, a TV(total variation) loss function was applied to
eliminate noise. Our method was trained on the GladNet dataset, known for its
diverse collection of low-light images, tested against the Low-Light dataset,
and evaluated using the ExDark dataset for downstream tasks, demonstrating
competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Photographs taken with less-than-ideal exposure settings often display poor
visual quality. Since the correction procedures vary significantly, it is
difficult for a single neural network to handle all exposure problems.
Moreover, the inherent limitations of convolutions, hinder the models ability
to restore faithful color or details on extremely over-/under- exposed regions.
To overcome these limitations, we propose a Macro-Micro-Hierarchical
transformer, which consists of a macro attention to capture long-range
dependencies, a micro attention to extract local features, and a hierarchical
structure for coarse-to-fine correction. In specific, the complementary
macro-micro attention designs enhance locality while allowing global
interactions. The hierarchical structure enables the network to correct
exposure errors of different scales layer by layer. Furthermore, we propose a
contrast constraint and couple it seamlessly in the loss function, where the
corrected image is pulled towards the positive sample and pushed away from the
dynamically generated negative samples. Thus the remaining color distortion and
loss of detail can be removed. We also extend our method as an image enhancer
for low-light face recognition and low-light semantic segmentation. Experiments
demonstrate that our approach obtains more attractive results than
state-of-the-art methods quantitatively and qualitatively.Comment: Accepted by ACM MM 202
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