Low Illumination Image Enhancement Research Articles

Natural low-illumination image enhancement based on dual-channel prior information

This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

A novel image enhancement method using retinex-based illumination map weighted guided filtering

Halo artifact, edge detail loss and noise amplification are the main problems in low illumination image enhancement, an image enhancement algorithm combining Retinex and illumination map weighted guided filtering is proposed. The traditional defogging physical models only enhance the images based on dark channels prior, resulting in different depths of field in local areas, and it can lead to some problems such as image overexposure and halo artifacts. To solve this problem, the method of combining light and dark channels is adopted to calculate the atmospheric light value and transmittance. For the problem that edge information is easily lost, the illumination gradient domain weighted guided filtering is utilized to improve the thinning transmittance. Experimental results with the proposed method have obvious improvement in denoising, halo elimination, brightness adjustment and edge preservation in the low-illumination image under different conditions.

Event-Based Low-Illumination Image Enhancement

Event-Based Low-Illumination Image Enhancement

Low-illumination image enhancement with logarithmic tone mapping

Abstract For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.

Improved Retinex algorithm for low illumination image enhancement in the chemical plant area

Due to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.

Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory.

Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.

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.

A Low-Illumination Enhancement Method Based on Structural Layer and Detail Layer.

Low-illumination image enhancement technology is a topic of interest in the field of image processing. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. In order to solve this problem, this paper proposed a low-illumination enhancement method based on structural and detail layers. Firstly, we designed an SRetinex-Net model. The network is mainly divided into two parts: a decomposition module and an enhancement module. Second, the decomposition module mainly adopts the SU-Net structure, which is an unsupervised network that decomposes the input image into a structural layer image and detail layer image. Afterward, the enhancement module mainly adopts the SDE-Net structure, which is divided into two branches: the SDE-S branch and the SDE-D branch. The SDE-S branch mainly enhances and adjusts the brightness of the structural layer image through Ehnet and Adnet to prevent insufficient or overexposed brightness enhancement in the image. The SDE-D branch is mainly denoised and enhanced with textural details through a denoising module. This network structure can greatly reduce computational costs. Moreover, we also improved the total variation optimization model as a mixed loss function and added structural metrics and textural metrics as variables on the basis of the original loss function, which can well separate the structure edge and texture edge. Numerous experiments have shown that our structure has a more significant impact on the brightness and detail preservation of image restoration.

An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer’s rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.

Adaptive Enhanced Detection Network for Low Illumination Object Detection

Any small environmental changes in the driving environment of a traffic vehicle can become a risk factor directly leading to major safety incidents. Therefore, it is necessary to assist drivers in automatically detecting risk factors during the driving process using algorithms. However, besides making it more difficult for drivers to judge environmental changes, the performance of automatic detection networks in low illumination scenarios can also be greatly affected and cannot be used directly. In this paper, we propose a risk factor detection model based on deep learning in low illumination scenarios and test the optimization of low illumination image enhancement problems. The overall structure of this model includes dual discriminators, encoder–decoders, etc. The model consists of two main stages. In the first stage, the input low illumination scene image is adaptively converted into a standard illumination image through a lighting conversion module. In the second stage, the converted standard illumination image is automatically assessed for risk factors. The experiments show that the detection network can overcome the impact of low lighting and has high detection accuracy.

Rain Removal Algorithm Based on Retinex in Low Light Automatic Driving Scenario

In the field of automatic driving, clear image is one of the important prerequisites for the decision-making system relying on computer vision to make correct decisions. Rainy days and low illumination environment are two common scenes that seriously affect image quality. Researchers have proposed many effective methods for these two research fields. However, few applications have been studied in the field of automatic driving comprehensively. To solve the problem, a low illumination image enhancement method in rainy days is proposed, which combines the two scenes and can be applied to the field of automatic driving. This algorithm takes good advantage of the fact that raindrops cannot appear in the same frame at different times to eliminate the noise of raindrops in the image .And its main process is to fuse continuous image frames by maximum method through multi-scale Gaussian filtering. Thus, the fused image is used as the illumination map and the original image to be denoised is used as the input of Multi-Scale Retinex(MSR) method.Finally, the reflection image which can be directly applied in automatic driving is obtained.

Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).

Low-Illumination Image Enhancement Based on Deep Learning Techniques: A Brief Review

As a critical preprocessing technique, low-illumination image enhancement has a wide range of practical applications. It aims to improve the visual perception of a given image captured without sufficient illumination. Conventional low-illumination image enhancement methods are typically implemented by improving image brightness, enhancing image contrast, and suppressing image noise simultaneously. Nevertheless, recent advances in this area are dominated by deep-learning-based solutions, and consequently, various deep neural networks have been proposed and applied to this field. Therefore, this paper briefly reviews the latest low-illumination image enhancement, ranging from its related algorithms to its unsolved open issues. Specifically, current low-illumination image enhancement methods based on deep learning are first sorted out and divided into four categories: supervised learning methods, unsupervised learning methods, semi-supervised learning methods, and zero-shot learning methods. Then, existing low-light image datasets are summarized and analyzed. In addition, various quality assessment indices for low-light image enhancement are introduced in detail. We also compare 14 representative algorithms in terms of both objective evaluation and subjective evaluation. Finally, the future development trend of low-illumination image enhancement and its open issues are summarized and prospected.

Evaluation of Real-Time Perception of Deformation State of Host Rocks in Coal Mine Roadways in Dusty Environment

Intelligent mining needs to achieve real-time acquisition of surrounding rock deformation data of roadways and analysis and provide technical support for intelligent mining construction. To solve problems such as significant error, information lag, and low acquisition frequency of surrounding rock monitoring technology at the current stage, a perception method, RSBV of roadway deformation situation, based on binocular vision is proposed, which realizes the dynamic, accurate real-time acquisition of host rocks’ relative deformation in a dusky environment. The low illumination image enhancement method is used to preprocess original images, which reduces the impact of low illumination and high dust; the K-medoids algorithm segments the target image, and the SIFT algorithm extracts feature points from the ROI (region of interest). The influence of eliminating background images on the feature point extraction is revealed, and the efficiency of feature extraction is improved; the method of SIFT feature-matching with epipolar constraints is studied, which improves the accuracy and speed of feature points. The roadway deformation characteristics are analyzed, and the reflective target is used as the monitoring point. A roadway deformation acquisition and analysis platform based on binocular vision is built in a dim environment. Zhang’s method is selected to calibrate the camera parameters, and stereo rectification is carried out for the target motion image. The adaptability of the RSBV method to different surrounding rock deformation scales is studied and compared with the measurement results of the SGBM algorithm. The results show that the error of the RSBV method is controlled within 1.6%, which is 2.88% lower than the average error of the SGBM algorithm. The average time for processing a group of binocular images is 1.87 s, which is only 20% of the SGBM algorithm. The research result provides a reliable theoretical basis for the real-time and accurate evaluation of the surrounding rock deformation mechanism.

Underwater low-light enhancement network based on bright channel prior and attention mechanism.

At present, there are some problems in underwater low light image, such as low contrast, blurred details, color distortion. In the process of low illumination image enhancement, there are often problems such as artifacts, loss of edge details and noise amplification in the enhanced image. In this paper, we propose an underwater low-light enhancement algorithm based on U-shaped generative adversarial network, combined with bright channel prior and attention mechanism, to address the problems. For the problems of uneven edges and loss of details that occurred in traditional enhanced images, we propose a two-channel fusion technique for the input channel. Aiming at the problems of brightness, texture and color distortion in enhanced images, we propose a feature extraction technique based on the attention mechanism. For the problems of noise in enhanced output images, we propose a multi-loss function to constrain the network. The method has a wide range of applications in underwater scenes with large depth. This method can be used for target detection or biological species identification in underwater low light environment. Through the enhancement experiment of underwater low light image, the proposed method effectively solves the problems of low contrast, blurred details, color distortion, etc. of underwater low light image. Finally, we performed extensive comparison experiments and completed ablation experiments on the proposed method. The experimental results show that the proposed method is optimal in human visual experience and underwater image quality evaluation index.

A dual channel decomposition and remapping fusion model for low illumination images with a wide field of view

In this paper, we investigate the problem of low illumination image enhancement under wide field of view condition. Our contribution consists of two parts: Firstly, we propose an adaptive two-parameter luminance remapping enhancement function (A2BREF) based on biological visual properties. Secondly, we propose a simple and effective noise suppression method for composite weighted map. In this paper, an image decomposition method based on the total variational energy is used to decompose the input image into structure and detail components. We apply luminance enhancement to the structure component and noise suppression to the detail component. The two components are processed in parallel without interfering with each other. First, we transform the structure component from RGB space to HSV space, and A2BREF combines the global and local luminance information of the input image to perform an adaptive luminance remapping of the V component. Then, the mapping result and the initial V component are fused with PCA in a specific order to enhance the image brightness. Secondly, the detail component contains a lot of noise and detail information of the input image. In this paper, we extract two weight maps from different stages of the detail component by means of improved adaptive Gaussian filters, and the two weight maps are combined to obtain the final weight map. After multiplying the weight maps with the initial detail components and stretching the contrast, the ideal detail components are obtained. Finally, the processed structure and detail components are combined to obtain the final result. The experimental results show that the proposed method has advantages over the contrast algorithm in terms of both subjective visual and objective metrics.

Double-function enhancement algorithm for low-illumination images based on retinex theory.

In order to solve the problems of noise amplification and excessive enhancement caused by low contrast and uneven illumination in the process of low-illumination image enhancement, a high-quality image enhancement algorithm is proposed in this paper. First, the total-variation model is used to obtain the smoothed V- and S-channel images, and the adaptive gamma transform is used to enhance the details of the smoothed V-channel image. Then, on this basis, the improved multi-scale retinex algorithms based on the logarithmic function and on the hyperbolic tangent function, respectively, are used to obtain different V-channel enhanced images, and the two images are fused according to the local intensity amplitude of the images. Finally, the three-dimensional gamma function is used to correct the fused image, and then adjust the image saturation. A lightness-order-error (LOE) approach is used to measure the naturalness of the enhanced image. The experimental results show that compared with other classical algorithms, the LOE value of the proposed algorithm can be reduced by 79.95% at most. Compared with other state-of-the-art algorithms, the LOE value can be reduced by 53.43% at most. Compared with some algorithms based on deep learning, the LOE value can be reduced by 52.13% at most. The algorithm proposed in this paper can effectively reduce image noise, retain image details, avoid excessive image enhancement, and obtain a better visual effect while ensuring the enhancement effect.

Low-Illumination Image Enhancement for Foreign Object Detection in Confined Spaces

Low-illumination image enhancement (LIIE) in confined spaces is a challenge faced in industrial inspection and diagnostics areas. Most existing methods cannot handle extremely dark images captured in confined spaces. This article reports a residual deep curve estimation network (ResZero) to enhance such dark images for foreign object detection (FOD). To reduce information loss when enhancing extremely dark images, residuals are added to the backbone of the proposed ResZero to pass forward features in the middle layers. To solve noise and color cast problems in the enhancement process, a new loss function is defined by considering the feature-preserving and RGB channel averaging simultaneously. Moreover, the dynamic range of the public datasets has been expanded by adding extremely dark images captured in confined spaces. Experiments with various extremely dark scenes demonstrated that the enhanced image’s quality average score (BRISQUE) of the proposed ResZero is 22.25 better than all other methods(0.22 higher than the second place). In FOD experiments, the proposed method is with the highest precision.

Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

Images captured under low-illumination conditions usually suffer from severe degradations, such as fading and low contrast, drastically affecting the performance of systems relying on images under low-illumination conditions. To address such problems, this study proposes a linear contrast enhancement network (LCENet) for low-illumination image enhancement. It consists of three subnets: two encoder–decoder-based subnets for gradient map restoration and brightness enhancement, respectively, and a backbone network for adaptive brightness and contrast adjustment. In addition, a linear contrast enhancement adaptive instance normalization (LCEAIN) module with linear contrast enhancement ability is proposed in the backbone network, which can avoid the problem of ignoring contrast enhancement when enhancing image brightness. Considerable evaluations on both synthetic and real low-illumination images show that the proposed method performs favorably against other existing similar methods. Moreover, our method can handle complex low-illuminance conditions and has good generalization for low-illuminance scenes with backlighting, night scenes with light sources, as well as underwater scenes with low illuminance. Code: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhouzhaorun/LCENet</uri> .

Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection

Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.