A Semi-Supervised Network Framework for low-light image enhancement

Chen, Jin; Wang, Yong; Han, Yujuan

doi:10.1016/j.engappai.2023.107003

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Yang et al [47] proposed a low-light image enhancement method using a deep recursive band network (DRBN). Chen et al [48] put forward a semi-supervised network framework (SSNF) to enhance low-light images. Malik and Soundararajan [49] proposed semi-supervised learning for low-light image restoration via quality-assisted pseudo-labeling.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

BézierCE: Low-Light Image Enhancement via Zero-Reference Bézier Curve Estimation

Gao,

Zhao,

Han

et al. 2023

Sensors

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Due to problems such as the shooting light, viewing angle, and camera equipment, low-light images with low contrast, color distortion, high noise, and unclear details can be seen regularly in real scenes. These low-light images will not only affect our observation but will also greatly affect the performance of computer vision processing algorithms. Low-light image enhancement technology can help to improve the quality of images and make them more applicable to fields such as computer vision, machine learning, and artificial intelligence. In this paper, we propose a novel method to enhance images through Bézier curve estimation. We estimate the pixel-level Bézier curve by training a deep neural network (BCE-Net) to adjust the dynamic range of a given image. Based on the good properties of the Bézier curve, in that it is smooth, continuous, and differentiable everywhere, low-light image enhancement through Bézier curve mapping is effective. The advantages of BCE-Net’s brevity and zero-reference make it generalizable to other low-light conditions. Extensive experiments show that our method outperforms existing methods both qualitatively and quantitatively.

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Section: Semi-supervised Learningmentioning

confidence: 99%

BézierCE: Low-Light Image Enhancement via Zero-Reference Bézier Curve Estimation

Gao,

Zhao,

Han

et al. 2023

Sensors

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Enhancing low‐light images with lightweight fused fixed‐directional filters network

2024

IET Image Processing

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Deep learning has made significant progress in the field of low‐light image enhancement. However, challenges remain, such as the substantial parameter consumption required for effective image enhancement. Inspired by multi‐scale geometric transformations in image detail enhancement, a novel model called the fixed‐directional filters network is proposed. Fixed‐directional filters network takes the original image as input and employs multiple branches for parallel processing. One branch uses conventional convolutional layers to extract features from the original image, while the other branches apply non‐linear mapping layers based on wavelet transforms. These wavelet transform branches capture the multi‐scale information of the image by combining different directions and convolutional kernels and utilize a trainable custom gamma mapping layer for non‐linear modulation to enhance specific regions of the image. The feature maps processed by each branch are merged through concatenation operations and then passed through convolutional layers to output the enhanced image. Using trainable mapping functions alone to enhance details significantly reduces the reliance on convolutional layers, effectively lowering the model's parameter count to only 13k parameters. Additionally, experiments demonstrate that fixed‐directional filters network significantly improves image quality, particularly in capturing image details and enhancing image contrast.

show abstract

A Semi-Supervised Network Framework for low-light image enhancement

Cited by 2 publications

References 44 publications

BézierCE: Low-Light Image Enhancement via Zero-Reference Bézier Curve Estimation

BézierCE: Low-Light Image Enhancement via Zero-Reference Bézier Curve Estimation

Enhancing low‐light images with lightweight fused fixed‐directional filters network

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