Low-light Imaging Research Articles

Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration

Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration

Color and Luminance Separated Enhancement for Low-Light Images with Brightness Guidance.

Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately 4.4% on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model's generalizability across multiple real-world datasets.

Lightweight two-stage transformer for low-light image enhancement and object detection

In low-light conditions, due to the loss of image details the visual tasks are challenging. To achieve real-time image enhancement and improve the accuracy of object detection task, we propose a lightweight two-stage Transformer. First, we use dynamic convolution to improve the adaptability of the network to different samples and preliminarily enhance the image through predicting the multiplicative and additive maps of the least squares method. In the second stage, we propose a FFT-Guidance Block (FGB) to obtain frequency components for explicit modeling, guiding the recovery of image potential information. In addition, joint our model with YOLOv3 to build a dark object detection framework, and we only use normal detection loss to simplify the training process. On the LOLv2 dataset, our model achieves advanced results. The enhanced model maintains good performance while the parameters are only 0.050M, and increase the accuracy of the downstream object detection task. The detection framework reaches 77.9% and 60.2 in mAP and FPS respectively on ExDark dataset, which can be better robust in dark conditions.

A lightweight feature activation guided multi-receptive field attention network for light compensation

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks.

Channel Self-Attention Based Low-Light Image Enhancement Network

Low-light image enhancement is crucial in applications such as traffic safety and medical imaging. Besides having characteristics like low luminance and poor visibility, low-light images are inevitably affected by noise. Noise not only covers image details, introduces artifacts, and decreases image quality, but also interferes with downstream computer vision tasks like object detection, image segmentation, and object tracking. Previous methods in image enhancement often overlook noise handling or fail to accurately suppress noise in adaptive denoising processes, resulting in more severe noise artifacts in the enhanced images. To effectively suppress noise, this paper proposes a Channel Self-Attention Based Low-Light Image Enhancement Network (CAENet), which leverages Transformers and CNNs to model long-range and short-range pixel dependencies, extract global and local features, and construct a Noise Suppression Transformer Block that adaptively suppresses noise regions guided by signal-to-noise ratio priors and attention maps. After adaptive noise suppression, the resulting images exhibit fewer noise artifacts and improved details. The experimental results show that the network in this paper outperforms other state-of-the-art methods overall on five representative paired datasets as well as six unpaired datasets, improving the image quality while effectively suppressing the noise.

A Novel Progressive Enhancement of Low Light Raw Images

Low-light imaging on mobile devices is often difficult due to the issue of not enough light passing through the small aperture, resulting in poor quality images. Most previous work on low-resolution images has focused on a single task, such as illumination, color correction, or noise removal; Noise removal function based on short and long images of the camera model. This technique is less efficient and general in real-world environments that require special camera integration and restoration. In this paper, we propose a lighting system that can integrate illumination variation, color correction, and noise removal to solve this problem. Considering the difficulty of obtaining model-specific data and the maximum content of the resulting image, we created two branches: the coefficient estimation branch and the integration branch. While the computer estimator operates in the sparsely resolved space and estimates the coefficients developed by pairwise learning, the collaborative system operates in the fully resolved space, providing step-by-step integration and denoising. Compared to existing methods, our framework does not need to remember as much information when switching to another camera model, which reduces the effort required to benefit our usage pattern. Through extensive testing, we demonstrate its great potential in real-world low-light applications.

An Adaptive Semantic Segmentation Network for Adversarial Learning Domain Based on Low-Light Enhancement and Decoupled Generation

Nighttime semantic segmentation due to issues such as low contrast, fuzzy imaging, and low-quality annotation results in significant degradation of masks. In this paper, we introduce a domain adaptive approach for nighttime semantic segmentation that overcomes the reliance on low-light image annotations to transfer the source domain model to the target domain. On the front end, a low-light image enhancement sub-network combining lightweight deep learning with mapping curve iteration is adopted to enhance nighttime foreground contrast. In the segmentation network, the body generation and edge preservation branches are implemented to generate consistent representations within the same semantic region. Additionally, a pixel weighting strategy is embedded to increase the prediction accuracy for small targets. During the training, a discriminator is implemented to distinguish features between the source and target domains, thereby guiding the segmentation network for adversarial transfer learning. The proposed approach’s effectiveness is verified through testing on Dark Zurich, Nighttime Driving, and CityScapes, including evaluations of mIoU, PSNR, and SSIM. They confirm that our approach surpasses existing baselines in segmentation scenarios.

Lit me up: A reference free adaptive low light image enhancement for in-the-wild conditions

Images captured with different devices in uneven conditions (e.g., invariable lighting, low lighting, weather changes, exposure time, etc.) often lead to low image visibility and poor color and contrast, affecting the performance of computer vision and pattern recognition applications. The pre-trained convolutional neural networks (CNNs) solely rely on the training data and lack adaptation due to the uncertainty in the lighting conditions. Moreover, capturing large-scale datasets to train CNNs also raises the computational complexity and overall cost. This work integrates the knowledge and data and proposes a two-stage Uneven-to-Enliven network (U2E-Net), which rapidly learns to see in uneven conditions. A multiple-layered Uneven network learns to distinguish the reflection and illumination in the input images, and an encoder–decoder-based Enliven-Net contextualizes the illumination information. A key component in such ill-posed problems is to obtain information from priors and pairs; however, we present the compelling idea of information trade-off followed by decomposition consistency, thereby progressively improving the visual quality with the subsequent enhancement operations. To this end, we proposed a two-faceted framework that can work without depending on the data type. A novel color and contrast preservation strategy (CPS) is proposed following the decomposition of input data. CPS is integrated within the network to extract contrast in the darkest background regions.

Content-illumination coupling guided low-light image enhancement network

Current low-light enhancement algorithms fail to suppress noise when enhancing brightness, and may introduces structural distortion and color distortion caused by halos or artifacts. This paper proposes a content-illumination coupling guided low-light image enhancement network (CICGNet), it develops a truss topology based on Retinex as backbone to decompose low-light image component in an end-to-end way. The preservation of content features and the enhancement of illumination features are carried out along with depth and width direction of the truss topology. Each submodule uses the same resolution input and output to avoid the introduction of noise. Illumination component prevents misestimation of global and local illumination by using pre- and post-activation features at different depth levels, this way could avoid possible halos and artifacts. The network progressively enhances the illumination component and maintains the content component stage-by-stage. The proposed algorithm demonstrates better performance compared with advanced attention-based low-light enhancement algorithms and state-of-the-art image restoration algorithms. We also perform extensive ablation studies and demonstrate the impact of low-light enhancement algorithm on the downstream task of computer vision. Code is available at: https://github.com/Ruini94/CICGNet.

FRR-NET: a fast reparameterized residual network for low-light image enhancement

FRR-NET: a fast reparameterized residual network for low-light image enhancement

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Night Time Image Enhancement

Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to learn the mapping between the two modalities. The architecture of MIRNet incorporates convolutional layers with residual connections to effectively capture low-light image features and generate visually pleasing enhancements. We evaluate the performance of our approach on a diverse range of nighttime scenes and compare the results against existing methods. Our experiments demonstrate that MIRNet produces superior results in enhancing nighttime images, significantly improving visibility, reducing noise, and preserving image details. The proposed approach holds promise for real-world applications where high-quality nighttime imagery is essential for decision-making and visual analysis. Keywords: Night time image enhancement, MIRNet, Deep learning, Low-light imaging, Image Processing,Convolutional neural networks (CNNs),Residual connections, Supervised learning, Dataset preparation,Imagequalityimprovement,Noisereduction,Visibilityenhancement,Surveillance,Autonomousdriving,Photography.

Swin enhancer: A low-light smart meter image enhancement method with a multi-layered window attention network

Accurate and efficient identification of visual faults in smart meters is significant to the stable operation of the power acquisition system. However, the electricity meter images collected in dim environments have low brightness problems and lack of detail, which will impact subsequent fault identification based on computer vision. From an unsupervised perspective, this paper proposes a low-light smart meter image enhancement method, Swin enhancer. Originating from the Swin Transformer’s window attention mechanism, this paper designs a Multi-layer Swin Transformer Block (MSTB) to extract regional brightness features through the local window’s attention calculation and provide varying degrees of illumination compensation. At the same time, a shift window mechanism is introduced to interact features among the regions and reduce the possibility of overexposure. Extensive experiments on several benchmark and electric meter datasets demonstrate that our method outperforms state-of-the-art methods on multiple image evaluation metrics.

Progressive feature fusion for SNR-aware low-light image enhancement

Restoring image quality in low-light environments is an intriguing topic. While deep learning models have made significant strides in low-light enhancement, most models do not take into account the inherent characteristics of objects themselves. In this paper, we use the characteristics of the image itself to construct a Signal-to-Noise Ratio (SNR) map that guides the signal space variation to dynamically stretch the pixel values. Specifically, we propose a novel signal-to-noise ratio image-guided enhancement framework that uses the feature information of the original image to guide spatial variations in the image. It involves step-wise guidance for image feature fusion, gradually emphasizing high-frequency feature information within the image. Meanwhile, we introduced a texture optimization module that utilizes the feature information extracted by the feature fusion module to address the issues of overexposure and detail loss. We performed qualitative and quantitative evaluations on synthetic and real low-light image datasets to demonstrate the performance of our method. The experimental results show that our model outperforms other state-of-the-art methods (SOTA) in robust low-light enhancement, especially in processing images captured in complex scenes.

Joint Self-Supervised Enhancement and Denoising of Low-Light Images

Joint Self-Supervised Enhancement and Denoising of Low-Light Images

LET: a local enhancement transformer for low-light image enhancement

LET: a local enhancement transformer for low-light image enhancement

Low-Light Image Enhancement With Multi-Scale Attention and Frequency-Domain Optimization

Low-Light Image Enhancement With Multi-Scale Attention and Frequency-Domain Optimization

A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation

A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.

Multi-Frequency Field Perception and Sparse Progressive Network for low-light image enhancement

Images taken in low-light conditions often suffer from various types of degradation. While most current methods primarily focus on spatial domain information to address these degradation issues, they often overlook the importance of frequency domain information. In order to better solve the degradation problems of low-light images, the Multi-Frequency Field Perception and Sparse Progressive Network (MFSPNet) for low-light image enhancement is proposed through Leveraging the complementary strengths of the frequency domain and the spatial domain, aiming to tackle the challenges of degraded images in intricate scenarios. Specifically, we propose the frequency domain field feature filtering (FDFF) module, which utilizes image frequency distribution information to address issues such as noise and artifacts in low-light images while complementing the spatial domain. Subsequently, we embed different scales of FDFF into four heterogeneous branches to flexibly handle features at various levels of complexity. Furthermore, we design a sparse wing-shaped transformer block (SWTB) that enhances the focus on critical information and complex multi-scale details through adaptive sparse attention unit (ASAU) and illumination multi-scale fusion feedforward network (IMF-FN). In addition, we propose a progressive enhancement strategy for self-knowledge sublimation to gradually improve image quality. At last, we comprehensively assess the proposed network across multiple datasets. Compared to other methods, our approach achieved the highest PSNR scores, with improvements of 3.014 dB and 0.215 dB, respectively, over the next best results. Additionally, our method exhibited the highest SSIM gain. Abundant experimental outcomes demonstrate that our approach outperforms numerous present low-light image enhancement approaches in both objective evaluation metrics and subjective visual effects, showcasing outstanding performance and significant potential.

MFHOD: Multi-modal image fusion method based on the higher-order degradation model

The task of multimodal image fusion aims to preserve the respective advantages of each modality, such as the detailed texture information from visible light images and the salient target information from infrared images. However, images in real environments are often not perfect high-quality images but are affected by various degradation factors such as noise, blur, and glare. Existing unsupervised multimodal image fusion algorithms use undegraded source images as input to fusion networks and generate high-quality fusion images through complex fusion strategies. This single linear learning approach from high-quality to high-quality cannot be applied to the fusion task of degraded multimodal images in real environments. To address this issue, this paper proposes an end-to-end multimodal image fusion network based on a high-order local degradation model (MFHOD). Firstly, inspired by the idea of probabilistic degradation, we propose a high-order local random degradation model (HODM), which inputs the source multimodal images into the degradation model to obtain degraded images before feeding them into the network. Secondly, we design a simple and efficient dual-branch feature extraction encoder to extract deep features from images. Then, from the perspectives of image pixels, brightness, and gradients, we propose an improved composite loss composed of multiple loss functions to constrain network training. Finally, we propose a L2-norm fusion strategy for preserving brightness information in low-light nighttime images. Our MFHOD demonstrates good performance on infrared and visible light image datasets as well as medical image datasets. Experimental results show that MFHOD can effectively suppress the effects of glare, noise, and smoke in adverse environments, and also improve the quality of fusion images in low-light and nighttime environments.