Low-light Image Enhancement Algorithm Research Articles

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-light image enhancement algorithms play a crucial role in revealing details obscured by darkness in images and substantially improving overall image quality. However, existing methods often suffer from issues like color or lightness distortion and possess limited scalability. In response to these challenges, we introduce a novel low-light image enhancement algorithm leveraging a cell vibration energy model and lightness difference. Initially, a new low-light image enhancement framework is proposed, building upon a comprehensive understanding and analysis of the cell vibration energy model and its statistical properties. Subsequently, to achieve pixel-level multi-lightness difference adjustment and exert control over the lightness level of each pixel independently, a lightness difference adjustment strategy is introduced utilizing Weibull distribution and linear mapping. Furthermore, to expand the adaptive range of the algorithm, we consider the disparities between HSV space and RGB space. Two enhanced image output modes are designed, accompanied by a thorough analysis and deduction of the relevant image layer mapping formulas. Finally, to enhance the reliability of experimental results, certain image faults in the SICE database are rectified using the feature matching method. Experimental results showcase the superiority of the proposed algorithm over twelve state-of-the-art algorithms. The resource code of this article will be released at https://github.com/leixiaozhou/CDEGmethod.

A Low Cost FPGA Implementation of Retinex Based Low-Light Image Enhancement Algorithm

A Low Cost FPGA Implementation of Retinex Based Low-Light Image Enhancement Algorithm

Research on Unsupervised Low-Light Railway Fastener Image Enhancement Method Based on Contrastive Learning GAN

The railway fastener, as a crucial component of railway tracks, directly influences the safety and stability of a railway system. However, in practical operation, fasteners are often in low-light conditions, such as at nighttime or within tunnels, posing significant challenges to defect detection equipment and limiting its effectiveness in real-world scenarios. To address this issue, this study proposes an unsupervised low-light image enhancement algorithm, CES-GAN, which achieves the model’s generalization and adaptability under different environmental conditions. The CES-GAN network architecture adopts a U-Net model with five layers of downsampling and upsampling structures as the generator, incorporating both global and local discriminators to help the generator to preserve image details and textures during the reconstruction process, thus enhancing the realism and intricacy of the enhanced images. The combination of the feature-consistency loss, contrastive learning loss, and illumination loss functions in the generator structure, along with the discriminator loss function in the discriminator structure, collectively promotes the clarity, realism, and illumination consistency of the images, thereby improving the quality and usability of low-light images. Through the CES-GAN algorithm, this study provides reliable visual support for railway construction sites and ensures the stable operation and accurate operation of fastener identification equipment in complex environments.

A Low-Light Image Enhancement Algorithm Incorporating Cross-Mixed Attention and Receptive Field Expansion Mechanism

A Low-Light Image Enhancement Algorithm Incorporating Cross-Mixed Attention and Receptive Field Expansion Mechanism

Low-light image enhancement algorithm based on illumination and scene texture attention map

Low-light image enhancement algorithm based on illumination and scene texture attention map

Research on the Application of Convolutional Neural Network Model in Night Surveillance Video Image Enhancement

With the popularization of professional digital imaging equipment, digital image processing is popularly used in many fields such as industrial production, video surveillance, intelligent transportation, remote sensing and monitoring, and plays a significant role. In low illumination environments such as cloudy days, nights, indoor and object occlusion, imaging devices often capture images with low brightness and contrast, severe loss of detail information, and a large amount of noise. Enhancing low illumination images can enhance their clarity, highlight the texture details of the scene, greatly enhance the quality of the image, and provide data quality assurance for completing tasks such as target recognition and tracking, image segmentation, etc. This paper proposes a low light image enhancement algorithm based on CNN model to address the problem of low brightness and unclear monitoring video images in nighttime scenes due to lighting conditions. This algorithm can effectively improve the quality of low light images and exhibit superiority on multiple public datasets. The algorithm proposed in this article not only effectively improves the brightness of the image, but also enhances the detail clarity of the image to a certain degree, and can avoid color distortion and halo phenomena to a certain degree.

Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory

To address the challenges of low-light images, such as low brightness, poor contrast, and high noise, a network model based on deep learning and Retinex theory is proposed. The model consists of three modules: image decomposition, illumination enhancement, and color restoration. In the image decomposition module, dilated convolutions and residual connections are employed to mitigate the issue of detail loss during the decomposition process. The illumination enhancement module utilizes a set of mapping curves to enhance the illumination map. The color restoration module employs a weighted fusion of a 3D lookup table (3DLUT) to mitigate color distortion in the images. The experimental results demonstrate that the proposed algorithm effectively improves the brightness and contrast of low-light images while addressing the issues of detail loss and color distortion. Compared to other algorithms, it achieves better subjective and objective evaluations.

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

Existing supervised learning-based low-light image enhancement algorithms treat all degradations as a whole, resulting in limited enhancement performance. It is difficult for fully unsupervised learning to recover more hidden details, making the augmented results unsatisfactory for visual needs. To overwhelm the limitations of supervised and unsupervised learning, we propose a Semi-Supervised Network Framework (SSNF) to enhance low-light images. Specifically, we decouple the low-light image enhancement task into two stages. In the first stage of the SSNF, we employ methods based on information entropy and Retinex to improve the visibility of images. It is worth mentioning that this stage is a lightweight self-supervised network, which only needs to input low-light images and undergo minute-level training to achieve brightness improvement. In the second stage of the SSNF, we utilize U-Net and residual networks to remove problems such as noise and degradation existing in the first-stage enhancement results, thereby improving the visual properties of the enhanced images. It overwhelms the challenge of dealing with low-light images directly. We conduct extensive experiments on datasets such as LOL, synthetic, DICM, etc. The experimental results show that SSNF exhibits better visual effects and outperforms other advanced methods in performance metrics.

Low illumination image enhancement based on attention mechanism and global illumination estimation

Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.

CUI-Net: a correcting uneven illumination net for low-light image enhancement

Uneven lighting conditions often occur during real-life photography, such as images taken at night that may have both low-light dark areas and high-light overexposed areas. Traditional algorithms for enhancing low-light areas also increase the brightness of overexposed areas, affecting the overall visual effect of the image. Therefore, it is important to achieve differentiated enhancement of low-light and high-light areas. In this paper, we propose a network called correcting uneven illumination network (CUI-Net) with sparse attention transformer and convolutional neural network (CNN) to better extract low-light features by constraining high-light features. Specifically, CUI-Net consists of two main modules: a low-light enhancement module and an auxiliary module. The enhancement module is a hybrid network that combines the advantages of CNN and Transformer network, which can alleviate uneven lighting problems and enhance local details better. The auxiliary module is used to converge the enhancement results of multiple enhancement modules during the training phase, so that only one enhancement module is needed during the testing phase to speed up inference. Furthermore, zero-shot learning is used in this paper to adapt to complex uneven lighting environments without requiring paired or unpaired training data. Finally, to validate the effectiveness of the algorithm, we tested it on multiple datasets of different types, and the algorithm showed stable performance, demonstrating its good robustness. Additionally, by applying this algorithm to practical visual tasks such as object detection, face detection, and semantic segmentation, and comparing it with other state-of-the-art low-light image enhancement algorithms, we have demonstrated its practicality and advantages.

Low light image enhancement algorithm based on improved multi-objective grey wolf optimization with detail feature enhancement

This article aims to enhance low-light images by proposing a novel algorithm called MoGDF. It departs from the traditional approach of using deep learning with low-light and normal images as training data. Instead, exposure images at five different exposure scales are generated using a proposed model called EIPM, which consists of an improved Gamma function. Then it introduces a feature extraction network called FMEM, which leverages an improved PFPN structure to enhance the fusion of multi-scale information and strengthen the connection between high-level and low-level semantic information. Finally, it optimizes the fusion weights using an improved multiple-objective grey wolf optimization algorithm with comprehensive improvements in the convergence factor updating strategy, adaptive inertia weight strategy, and individual position strategy. The experiments show that the MoGDF outperforms the current state-of-the-art algorithms in terms of color restoration and detail preservation, and has significant advantages in objective and subjective evaluation metrics, with improvements of 5.69%, 2.60%, 1.11%, and 6.67% in PSNR, SSIM, NIQE, and LPIPS, respectively.

DMPH-Net: a deep multi-scale pyramid hybrid network for low-light image enhancement with attention mechanism and noise reduction

Aiming at the problems of color bias and noise enhancement on the output image by the traditional low-light image enhancement algorithm, we propose a deep multi-scale pyramid hybrid network (DMPH-Net) algorithm that fuses attention mechanism and multi-scale pyramid is proposed in this paper. The algorithm uses DecomNet to decompose reflectance and light components, and uses multi-scale illumination attention module to fuse light and reflectance for the decomposed low-light reflectance to improve the realism and details of reflectance; by using five-layer feature pyramid and kernel selection in PRID-net module to achieve the fusion of contextual information between different scale feature layers, while effectively removing the enhanced of noise, and the added color loss effectively suppresses the color bias of the output image; using multi-scale cascading and channel attention mechanisms to adjust the illumination and fuse the illumination ratios, effectively enhancing the brightness, texture, and other feature information in the image. The DMPH-Net algorithm is experimentally validated on LOL and no-reference LIME, MEF, and NPE datasets, and the objective evaluation metrics PSNRuparrow , SSIMuparrow , LIPIPSdownarrow , and NIQEdownarrow are 23.3772, 0.8442, 0.1386, and 3.5966 on LOL dataset. The objective evaluation metrics NIQE on the reference-free datasets LIME, NPE, and MEF are 3.0735, 3.1711, and 2.9464, respectively. The experiments show that the DMPH-Net algorithm maintains high image details and textures in image enhancement and denoising, effectively enhances low-light images, and reduces noise and color bias of images. Compared with RUAS, UnRetinex-Net, and other enhancement algorithms, it improves in objective evaluation metrics PSNR, SSIM, LIPIPS, and NIQE.

Research on Improved YOLOv5 for Low-Light Environment Object Detection

Object detection in low-light scenarios has been widely acknowledged as a significant research area in the field of computer vision, presenting a challenging task. Aiming at the low detection accuracy of mainstream single-stage object detection models in low-light scenarios, this paper proposes a detection model called DK_YOLOv5 based on YOLOv5, specifically designed for such scenarios. First, a low-light image enhancement algorithm with better results is selected to generate enhanced images that achieve relatively better visual effects and amplify target features. Second, the SPPF layer is improved to an R-SPPF module with faster inference speed and stronger feature expression ability. Next, we replace the C3 module with the C2f module and incorporate an attention mechanism to develop the C2f_SKA module, enabling richer gradient information flow and reducing the impact of noise features. Finally, the model detection head is replaced with a decoupled head suitable for the object detection task in this scenario to improve model performance. Additionally, we expand the Exdark dataset to include low-light data of underground mine scenario targets, named Mine_Exdark. Experimental results demonstrate that the proposed DK_YOLOv5 model achieves higher detection accuracy than other models in low-light scenarios, with an mAP0.5 of 71.9% on the Mine_Exdark dataset, which is 4.4% higher than that of YOLOv5.

Enhancement algorithm of low illumination image for UAV images inspired by biological vision

To address the issue of low brightness, high noise and obscure details of UAV aerial low-light images, this paper proposes an UAV aerial low-light image enhancement algorithm based on dual-path inspired by the dual-path model in human vision system. Firstly, a U-Net network based on residual element is constructed to decompose UAV aerial low-light image into structural path and detail path. Then, an improved generative adversarial network (GAN) is proposed to enhance the structural path, and edge enhancement module is added to enhance the edge information of the image. Secondly, the noise suppression strategy is adopted in detail path to reduce the influence of noise on image. Finally, the output of the two paths is fused to obtain the enhanced image. The experimental results show that the proposed algorithm visually improves the brightness and detail information of the image, and the objective evaluation index is better than the other comparison algorithms. In addition, this paper also verifies the influence of the proposed algorithm on the target detection algorithm under low illumination conditions, and the experimental results show that the proposed algorithm can effectively improve the performance of the target detection algorithm.

Low-Light Image Enhancement Algorithm Based on Multi-Scale Concat Convolutional Neural Network

Low-Light Image Enhancement Algorithm Based on Multi-Scale Concat Convolutional Neural Network

Low-light image enhancement algorithm based on multi-channel fusion attention network

Low-light image enhancement algorithm based on multi-channel fusion attention network

Low-light image enhancement algorithm based on light scattering attenuation model

é’ˆå¯¹ä¼ ç»Ÿå›¾åƒå¢žå¼ºç®—æ³•åœ¨æ€»ä½“ç §åº¦ä½Žä¸”å ‰ç §åˆ†å¸ƒä¸å‡çš„çŽ¯å¢ƒå† æ˜“é€ æˆå›¾åƒè¿‡åº¦å¢žå¼ºå’Œè‡ªç„¶åº¦ä¿å­˜èƒ½åŠ›å¼±çš„é—®é¢˜ï¼Œæå‡ºäº†ä¸€ç§åŸºäºŽå ‰çº¿æ•£å°„è¡°å‡æ¨¡åž‹çš„ä½Žç §åº¦å›¾åƒå¢žå¼ºç®—æ³•ã€‚é¦–å ˆï¼Œæœ¬æ–‡æå‡ºäº†å ‰çº¿æ•£å°„è¡°å‡æ¨¡åž‹ï¼Œè¯¥æ¨¡åž‹åˆç†è§£é‡Šäº†éžå‡åŒ€å ‰ç §åˆ†å¸ƒçš„æˆåƒè¿‡ç¨‹ï¼Œå ¶æ¬¡ï¼Œé€šè¿‡Max-RGBæ»¤æ³¢å™¨åˆ†åˆ«ä¼°è®¡æ¯ä¸ªåƒç´ çš„ç §åº¦ï¼Œåˆå§‹åŒ–ç §æ˜Žå›¾ï¼Œç„¶åŽï¼Œæ ¹æ®å ‰ç §éšå›¾åƒåˆ†å‰²åŒºåŸŸå ¥å°„è§’ã€å ¥å°„è·ç¦»å¹³æ»‘è¡°å‡çš„ç‰¹æ€§ï¼Œåˆ©ç”¨æ·±åº¦ä¿¡æ¯å’Œæ¢¯åº¦ä¿¡æ¯ä¼˜åŒ–å ‰ç §åˆ†é‡ï¼ŒèŽ·å¾—ç²¾ç¡®ç §æ˜Žå›¾ï¼›æ­¤å¤–ï¼Œåˆ©ç”¨æš—é€šé“å ˆéªŒä¸­çš„é€å°„çŽ‡æ‰€å ·æœ‰çš„å±€éƒ¨ç›¸ä¼¼æ€§æ¥çº¦æŸåå°„åˆ†é‡ä¼°è®¡ï¼›æœ€åŽï¼Œé€šè¿‡æå‡ºçš„å ‰çº¿æ•£å°„è¡°å‡æ¨¡åž‹èžåˆä¼˜åŒ–å ‰ç §åˆ†é‡å’Œåå°„åˆ†é‡ï¼ŒèŽ·å¾—æœ€ç»ˆçš„ä½Žç §åº¦å¢žå¼ºå›¾åƒã€‚é€šè¿‡ä¸ŽNPE，LIME，HE，ALSM，MSRCR，MF，WV_SIREç®—æ³•æ¯”è¾ƒï¼Œåœ¨ä¸åŒåœºæ™¯çš„ä½Žç §åº¦å›¾åƒä¸Šï¼Œæ— å‚è€ƒå„å‘å¼‚æ€§è´¨é‡è¯„ä¼°å™¨ï¼ˆBIQAA）最大为0.019 6，模糊检测累积概率（CPBD）最大为0.680 1ï¼Œæ— å‚è€ƒç©ºé—´åŸŸå›¾åƒè´¨é‡è¯„ä¼°å™¨ï¼ˆBRISQUE）最小为21.471 5，自然图像质量评估器（NIQE）最小为2.699 5ï¼Œç»¼åˆæ€§èƒ½ä¼˜äºŽå ¶ä»–å¯¹æ¯”ç®—æ³•ã€‚å®žéªŒç»“æžœè¡¨æ˜Žï¼Œæœ¬æ–‡ç®—æ³•èƒ½å¤Ÿæœ‰æ•ˆæŠ‘åˆ¶å™ªå£°ï¼Œæé«˜å›¾åƒå¯¹æ¯”åº¦å’Œäº®åº¦ï¼Œæ›´å¥½åœ°ä¿ç•™å›¾åƒè‡ªç„¶åº¦ã€‚

BrightFormer: A transformer to brighten the image

Low-light image enhancement algorithms need recover overall information of images, including local details and global information. However, existing image enhancement methods mainly focus on local details or global information. Therefore, it is challenging to balance the two aspects at the same time. This paper proposes a local dual-branch network (BrightFormer) for image enhancement that combines convolutions and transformers as to solution. The salient features of this paper are: (1) convolution is adopted to refine high-frequency information so that local features are preserved and propagated throughout the network; (2) combining gated parameters with prior information on illumination (ill-map) in self-attention can not only improves the flexibility of feature expression but also extract global features more easily; (3) the obtained local details and global features are fused by spatial and channel attention in Feature equalization fusion unit (FEFU); (4) a Deep feedforward network (DFN) is utilized to encode the location information between adjacent pixels, and the GELU activation function is used to retain useful features and eliminate useless features with an attention-like mechanism. Experimental results show that BrightFormer achieves competitive performance on quantitative metrics and visual perception on the datasets such as LOL, MEF and LIME etc.

A Survey of Low-light Image Enhancement

With the higher requirements of computer vision image enhancement of low-light image has become an important research content of computer vision. Traditional low-light image enhancement algorithms can improve image brightness and detailed visibility to varying degrees, but due to their strict mathematical derivation, such methods have bottlenecks and are difficult to break through their limits. With the development of deep learning and the birth of large-scale data sets, low-light image enhancement based on deep learning has become the mainstream trend. In this paper, first of all, the traditional low-light image enhancement algorithms are classified, summarized the improvement process of the traditional method, then the image enhancement method based on the deep learning are introduced, at the same time on the network structure and is suitable for the method of combing the network part, after the introduction to the experiment database and enhance image evaluation criteria. Based on the discussion of the above situation, combined with the actual situation, this paper points out the limitations of the current technology, and predicts its development trend.

Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the YOLOv4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective.