Unsupervised network for low-light enhancement
- URL: http://arxiv.org/abs/2306.02883v1
- Date: Mon, 5 Jun 2023 13:52:08 GMT
- Title: Unsupervised network for low-light enhancement
- Authors: Praveen Kandula, Maitreya Suin, and A. N. Rajagopalan
- Abstract summary: Supervised networks address the task of low-light enhancement using paired images.
We propose an unsupervised low-light enhancement network using contextguided illumination-adaptive norm (CIN)
We also propose a region-adaptive single input multiple output (SIMO) model that can generate multiple enhanced images from a single lowlight image.
- Score: 27.052207978537098
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Supervised networks address the task of low-light enhancement using paired
images. However, collecting a wide variety of low-light/clean paired images is
tedious as the scene needs to remain static during imaging. In this paper, we
propose an unsupervised low-light enhancement network using contextguided
illumination-adaptive norm (CIN). Inspired by coarse to fine methods, we
propose to address this task in two stages. In stage-I, a pixel amplifier
module (PAM) is used to generate a coarse estimate with an overall improvement
in visibility and aesthetic quality. Stage-II further enhances the saturated
dark pixels and scene properties of the image using CIN. Different ablation
studies show the importance of PAM and CIN in improving the visible quality of
the image. Next, we propose a region-adaptive single input multiple output
(SIMO) model that can generate multiple enhanced images from a single lowlight
image. The objective of SIMO is to let users choose the image of their liking
from a pool of enhanced images. Human subjective analysis of SIMO results shows
that the distribution of preferred images varies, endorsing the importance of
SIMO-type models. Lastly, we propose a low-light road scene (LLRS) dataset
having an unpaired collection of low-light and clean scenes. Unlike existing
datasets, the clean and low-light scenes in LLRS are real and captured using
fixed camera settings. Exhaustive comparisons on publicly available datasets,
and the proposed dataset reveal that the results of our model outperform prior
art quantitatively and qualitatively.
Related papers
- SAMPLING: Scene-adaptive Hierarchical Multiplane Images Representation
for Novel View Synthesis from a Single Image [60.52991173059486]
We introduce SAMPLING, a Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image.
Our method demonstrates considerable performance gains in large-scale unbounded outdoor scenes using a single image on the KITTI dataset.
arXiv Detail & Related papers (2023-09-12T15:33:09Z) - Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions.
The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result.
To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
arXiv Detail & Related papers (2023-08-10T07:53:06Z) - Visibility Enhancement for Low-light Hazy Scenarios [18.605784907840473]
Low-light hazy scenes commonly appear at dusk and early morning.
We propose a novel method to enhance visibility for low-light hazy scenarios.
The framework is designed for enhancing visibility of the input image via fully utilizing the clues from different sub-tasks.
The simulation is designed for generating the dataset with ground-truths by the proposed low-light hazy imaging model.
arXiv Detail & Related papers (2023-08-01T15:07:38Z) - Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss.
We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
arXiv Detail & Related papers (2023-07-09T08:29:19Z) - Unsupervised Low Light Image Enhancement Using SNR-Aware Swin
Transformer [0.0]
Low-light image enhancement aims at improving brightness and contrast, and reducing noise that corrupts the visual quality.
We propose a dual-branch network based on Swin Transformer, guided by a signal-to-noise ratio prior map.
arXiv Detail & Related papers (2023-06-03T11:07:56Z) - Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and
Transformer-Based Method [51.30748775681917]
We consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution.
We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms.
As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method.
arXiv Detail & Related papers (2022-12-22T09:05:07Z) - Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions.
Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
arXiv Detail & Related papers (2022-01-10T03:12:52Z) - Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement [3.4722706398428493]
Low-light images challenge both human perceptions and computer vision algorithms.
It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications.
This paper proposes a semantic-guided zero-shot low-light enhancement network which is trained in the absence of paired images.
arXiv Detail & Related papers (2021-10-03T10:07:36Z) - Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion.
Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
arXiv Detail & Related papers (2020-05-06T13:37:08Z) - Learning an Adaptive Model for Extreme Low-light Raw Image Processing [5.706764509663774]
We propose an adaptive low-light raw image enhancement network to improve image quality.
The proposed method has the lowest Noise Level Estimation (NLE) score compared with the state-of-the-art low-light algorithms.
The potential application in video processing is briefly discussed.
arXiv Detail & Related papers (2020-04-22T09:01:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.