An optimization-based approach to gamma correction parameter estimation for low-light image enhancement

Jeong, Inho; Lee, Chul

doi:10.1007/s11042-021-10614-8

Cited by 17 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison of this paper's method GS-AGC algorithm with other methods, such as Adaptive Gamma Correction (AGC) [21], Unsupervised Night Image Enhancement (Jin Y) [18], Zero-reference low-light enhancement (Zero-DCE) [20], HWMNet [43], and some other representative methods, was done through test images of two datasets. Then, the experimental results were evaluated globally and locally in terms of quality to test the glare suppression performance and dark light enhancement performance of the algorithms in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…For this reason, one of the main characteristics of a low-light image is dark light regions occupying the main part of the image. Some techniques commonly used in recent years include Zero-DCE [20], gamma correction [21], dark channel a priori [22], and histogram equalization [23]. Histogram equalization enhances an image by adjusting the grayscale of each pixel.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Li,

Wei,

Pan

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye’s luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Li,

Wei,

Pan

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The histogram equalization [1], gamma correction [2], and Retinex theory [3]-based conventional low-light image enhancing methods are among the more reliable ones. Among them, histogram equalization stretches the grayscale histogram of the image from a more concentrated grayscale interval to the entire grayscale range, expanding the range of grayscale values in the image and improving the image contrast and part of the detail effect, but it is prone to chromatic aberration and the grayscale merging loses the detail information.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

Xi,

Zhang,

Wang

2023

Applied Sciences

View full text Add to dashboard Cite

Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.

show abstract

“…Over the past decades, many low-light image enhancement methods have been proposed [ 5 , 6 ]. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Cao,

Huang

2024

Entropy

View full text Add to dashboard Cite

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.

show abstract

An optimization-based approach to gamma correction parameter estimation for low-light image enhancement

Cited by 17 publications

References 35 publications

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

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

Contact Info

Product

Resources

About