AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Li, Miao; Zhou, Dongming; Nie, Rencan; Xie, Shidong; Liu, Yanyu

doi:10.1049/ipr2.12173

Cited by 6 publications

(3 citation statements)

References 59 publications

(131 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Liu et al. [14] proposed a brightness‐aware network based on brightness‐aware attention and residue quantized codebook to achieve more natural and realistic enhancement. Combining Retinex theory with deep learning achieved unexpected results.…”

Section: Related Workmentioning

confidence: 99%

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

Han,

Xiao,

Yin

2024

IET Image Processing

View full text Add to dashboard Cite

In recent years, low‐light image enhancement has become increasingly active. However, in underground mine environments, acquiring high‐quality images is still challenging due to low light, low contrast, and occlusion. To address this problem, this study proposes a low‐light image enhancement method for underground mine based on generative adversarial networks (UM‐GAN), which aims to take full advantage of the ability of GAN to achieve the restoration of details, the reduction of noise, and the improvement of overall image quality. The model proposed in this paper is divided into three main parts. Initially, a generator network adopting an encoder–decoder structure is developed. Subsequently, a novel strategy is introduced to merge information by utilizing inverted greyscale images and low‐light images as inputs. Further image quality enhancement is achieved by incorporating a noise reduction module employing the diffusion model. To ascertain the efficacy of the UM‐GAN method, evaluations are conducted on diverse real‐world and synthetic datasets, juxtaposing the approach against superior methods. Through qualitative and quantitative comparative experiments, the method showcases noteworthy advancements through qualitative and quantitative comparative experiments, substantiating its effectiveness. This research provides new ideas and methods for overcoming image quality problems in underground mine environments and contributes to the development of underground mine image processing.

show abstract

Section: Related Workmentioning

confidence: 99%

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

Han,

Xiao,

Yin

2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Jiang et al [20] propose an attention guided U-net to generate enhanced image, but it is also based on retinex theory. The retinex theory still plays a vital role in recent researches [44,45].…”

Section: Image Quality Enhancementmentioning

confidence: 99%

Image quality enhancement using hybrid attention networks

Wang

Chen

Yan

2021

IET Image Processing

View full text Add to dashboard Cite

Image quality enhancement aims to recover rich details from degraded images, which is applied into many fields, such as medical imaging, filming production and autonomous driving. Deep convolutional neural networks (CNNs) have enabled rapid development of image quality enhancement. However, most existing CNN-based methods lack versatility when targeting different subtasks in terms of the design of networks. Besides, they often fail to balance precise spatial representations and necessary contextual information. To deal with these problems, this paper proposes a novel unified framework for low-light image enhancement, image denoising and image super-resolution. The core of this architecture is a residual hybrid attention block (RHAB), which consists of several dynamic down-sampling modules (DDM) and hybrid attention up-sampling modules (HAUM). Specifically, multi-scale feature maps are fully interacted with each other with the help of nested subnetworks so that both high-resolution spatial details and high-level contextual information can be combined to improve the representation ability of the network. Further, a hybrid attention network (HAN) is proposed and evaluations on three separate subtasks demonstrate its good performance. Extensive experiments on the authors' synthetic dataset, a more complex version, show that the authors' method achieve better quantitative and visual results compared to other state-of-the-art methods.

show abstract

“…Data-driven Methods. In recent years, deep neural networks have paved the way for the lowlight image enhancement task [8,15,19,20,22,24,39,40,[42][43][44]46]. According to the supervision level, the data-driven based enhancement models can be roughly divided into the supervised group, the semi-supervised group, and the unsupervised group.…”

Section: Related Workmentioning

confidence: 99%

Decoupled Low-light Image Enhancement

Hao¹,

Han²,

Guo³

et al. 2021

Preprint

View full text Add to dashboard Cite

The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to a sub-optimal performance. In this paper, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improving the appearance fidelity by suppressing the rest degeneration factors. The decoupled model facilitates the enhancement in two aspects. On the one hand, the whole low-light enhancement can be divided into two easier subtasks. The first one only aims to enhance the visibility. It also helps to bridge the large intensity gap between the low-light and normal-light images. In this way, the second subtask can be shaped as the local appearance adjustment. On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information. In the experiments, our model demonstrates the state-of-the-art performance in both qualitative and quantitative comparisons, compared with other low-light image enhancement models. In addition, the ablation studies also validate the effectiveness of our model in multiple aspects, such as model structure and loss function. The trained model is available at https://github.com/hanxuhfut/Decoupled-Low-light-Image-Enhancement.

show abstract

AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Cited by 6 publications

References 59 publications

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

Image quality enhancement using hybrid attention networks

Decoupled Low-light Image Enhancement

Contact Info

Product

Resources

About