Low-Light Image Enhancement: A Comparative Review and Prospects

Kim, Wonjun

doi:10.1109/access.2022.3197629

Cited by 32 publications

(18 citation statements)

References 76 publications

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“…Previous methods are usually based on hand-designed features and processing steps such as histogram equalization [ 7 , 8 ] and gamma transformation [ 9 ]. These methods are simple and fast, but they usually amplify noise while enhancing the image and often cannot restore the color and details of low-light images well [ 10 ]. The widely popular Retinex theory [ 11 ] provides an intuitive and easy-to-understand framework for LLIE by decomposing the image into reflection and illumination components [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Wang,

Cao,

Huang

2024

Entropy

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Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.

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Section: Introductionmentioning

confidence: 99%

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Wang,

Cao,

Huang

2024

Entropy

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“…Various techniques used to enhance images captured in low-light environments can be broadly categorized into two main groups: imagebased [6]- [17] and model-based methods [18]- [34]. See the review papers for more details [39]- [42].…”

Section: Introductionmentioning

confidence: 99%

“…Light noise In summary, model-based methods can effectively enhance low-light images but have limitations, such as the dependency on large amounts of labeled training data, the specific architecture and hyperparameters of the model, and the subjective nature of the ground truth used for training. It is a more challenging problem if other disruptive components, such as dark and light regions, glow, or artificial light sources, need to be handled, as the training datasets do not contain such degradations [42].…”

Section: Introductionmentioning

confidence: 99%

Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration

et al. 2023

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Images captured in low-light conditions often suffer from low brightness, low signal-to-noise ratio, low contrast, a narrow gray range, and color distortion, which can significantly impact human perception and limit the performance of various computer vision applications. Most existing low-light image restoration methods require assistance with a color cast, local over-exposure, glow, and artificial light sources. This paper proposes a new framework called RSD-Net, incorporating several innovative blocks, including a novel iterative Retinex network decomposition and enhancement algorithms, to improve the visibility and quality of images captured in low-light or nighttime conditions. We have extensively evaluated our proposed method on various benchmarking datasets and under different real-world scenarios, including challenging conditions such as glow, artificial light sources, low illumination, and noise. Moreover, we have evaluated our method on a face detection algorithm using extremely dark images and compared its performance with other state-of-the-art methods. The simulation results show that our proposed framework achieves a noticeable improvement compared to other low-quality image restoration techniques and enhances face detection accuracy in low-quality environments. The proposed framework has the potential to substantially impact human perception and enhance the performance of numerous computer vision applications.

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“…Therefore, for the processing of low-illumination images, digital image processing technology can be used to develop various enhancement algorithms that can improve the quality of low-illumination images. From the perspective of image processing, researchers propose many enhancement algorithms to solve the image quality problems [4,5].…”

Section: Introductionmentioning

confidence: 99%

Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory

Wen,

Nie,

et al. 2023

Sensors

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Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.

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Low-Light Image Enhancement: A Comparative Review and Prospects

Cited by 32 publications

References 76 publications

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration

Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory

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