Low Illumination Image Research Articles

Research on Defect Detection Method of Fusion Reactor Vacuum Chamber Based on Photometric Stereo Vision.

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber.

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.

Low-illumination and noisy bridge crack image restoration by deep CNN denoiser and normalized flow module

When applying deep learning and image processing techniques for bridge crack detection, the obtained images in real-world scenarios have severe image degradation problem. This study focuses on restoring low-illumination bridge crack images corrupted by noise to improve the accuracy of subsequent crack detection and semantic segmentation. The proposed algorithm consists of a deep CNN denoiser and a normalized flow-based brightness enhancement module. By taking the noise spectrum as an input, the deep CNN denoiser restores image at a broad range of noise levels. The normalized flow module, employs a conditional encoder and a reversible network to map the distribution of normally exposed images to a Gaussian distribution, effectively improving the image brightness. Extensive experiments have demonstrated the approach can usefully recover low-illumination images corrupted by noise compared to the state-of-the-art methods. Furthermore, the algorithm presented in this study can also be applied to other image quality restoration with high generalization and robust abilities. And the semantic segmentation accuracy of the restored image is significantly improved.

Bionic Scotopic Adaptation Transistors for Nighttime Low Illumination Imaging.

Human vision excels in perceiving nighttime low illumination due to biological feedforward adaptation. Replicating this ability in biomimetic vision using solid-state devices has been highly sought after. However, emulating scotopic adaptation, entailing a confluence of efficient photoexcitation and dynamic carrier modulation, presents formidable challenges. Here, we demonstrate a low-power and bionic scotopic adaptation transistor by coupling a light-absorption layer and an electron-trapping layer at the bottom of the semiconducting channel, enabling simultaneous achievement of efficient generation of free photocarriers and adaptive carrier accumulation within a single device. This innovation empowers our transistor to exhibit sensitivity-potentiated characteristics after adaptation, detecting scotopic-level illumination (0.001 lx) with exceptional photosensitivity up to 103 at low voltages below 2 V. Moreover, we have successfully replicated diverse scotopic vision functions, encompassing time-dependent visual threshold enhancement, light intensity-dependent adaptation index, imaging contrast enhancement for nighttime low illumination imaging, opening an opportunity for artificial night vision.

Image-Deblurring and Restoration: Exploring Advanced Techniques for Enhanced Visual Clarity

This study introduces an end-to-end scale-recurrent deep network for deblurring multi-modal medical images affected by motion artifacts. The network features a novel residual dense block with spatial-asymmetric attention, enhancing the recovery of salient information during medical image deblurring. Comprehensive evaluations demonstrate the method's superiority over existing techniques, showcasing its ability to remove blur without introducing visual artifacts. Integration into medical image analysis tasks, such as segmentation and detection, highlights accelerated performance. Additionally, a simple L0-regularized prior is proposed for text image deblurring, leveraging intensity and gradient properties. The method outperforms state-of-the-art text image deblurring algorithms, demonstrating effectiveness in deblurring low- illumination images without complex edge selection strategies. Our proposed end-to-end scale-recurrent deep network for medical image deblurring exhibits robust performance, outshining existing methods in both qualitative and quantitative evaluations. The inclusion of a spatial-asymmetric attention mechanism and a residual dense block enhances the network's ability to recover crucial information from blurred medical images. Furthermore, the seamless integration of our deblurring method into various medical image analysis tasks showcases its versatility and effectiveness in accelerating performance. On a different front, our contribution extends to text image deblurring, where a straightforward yet powerful L0-regularized prior based on intensity and gradient proves effective. This method not only surpasses state-of-the-art text image deblurring algorithms but also demonstrates applicability to challenging scenarios such as deblurring low-illumination images. The combined results highlight the potential impact of our approaches in advancing the quality and applicability of deblurring techniques in both medical imaging and text-related applications. Keywords—Multi-Scale, Unet, Deblurring, Fast Fourier Transform, ResBlock, Non-linear Activation Free Block, NFResnet, Charbonnier, Edge, Frequency Reconstruction.

Fusion of multiscale gradient domain enhancement and gamma correction for underwater image/video enhancement and restoration

Underwater images and video often suffer from poor visibility and color cast because light gets scattered and absorbed in water. To solve this problem, a novel method based on the fusion of multiscale gradient-domain enhancement and gamma correction is proposed for underwater images and video enhancement. Firstly, a white balance is used for color correction to the input underwater image and video. secondly, the color corrected underwater RGB images are converted to YCbCr color space. The Y-component of the image is decomposed into the base layer and detail layers using a Weighted Least Squares (WLS) filter. The base layer is enhanced by using gamma correction and detail layers are enhanced using the S-shape function in the gradient domain. The enhanced base layer and detail layers are fused. Finally, the fused image combined with Cb and Cr-component of YCbCr color space to obtained enhanced image. The proposed method has been implemented on the Underwater Image Enhancement Benchmark (UIEB) dataset, Underwater-45 (U45) dataset, and Dataset Real-world Underwater Video of Artifacts (DRUVA) based on the Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index Measure (SSIM), Measure of Enhancement (EME), Discrete Entropy (DE), Underwater Image Quality Measure (UIQM), and Underwater Color Image Quality Evaluation (UCIQE). To verify the efficacy of the proposed method, a qualitative and quantitative comparison has been conducted on the UIEB, U45, and DRUVA datasets. The result demonstrates that the proposed method outperforms the state-of-the-art method, in terms of PSNR, SSIM, EME, UIQM, and UCIQE parameters. Furthermore, the efficiency of proposed method tested on fog image and low illumination images.

A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique

Fuzzy logic systems play a significant role in various fields including image processing. Addressing the challenge of enhancing low-illumination images, which often contain a high degree of uncertain information, is a complex task. To overcome this issue, this study introduces a novel intuitionistic fuzzy generator for enhancing low-contrast images. Initially, a low-illumination image is considered and the image is then fuzzified using a fuzzification technique. The fuzzified image is converted into a intuitionistic fuzzy image using the proposed novel intuitionistic fuzzy generator and then applied a contrast limited adaptive histogram equalization method to obtain the enhanced image. The obtained results are compared with the existing studies. With the outcome of the performance measures such as entropy, no-reference low-light image enhancement evaluation and contrast improvement index, it is perceived that the proposed technique yields a finer results than the previous studies.

UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes

Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized, and an efficient image enhancement module is used to enhance the brightness of low illumination images. Then a future-aware module is constructed to train the tracking model using the contextual information of the target in the next frame for better robustness. Finally, the model updating stage involves adaptive filter updating and adaptive learning rate updating to enhance target tracking precision. The results of the comparison experiments with the state-of-the-art algorithms on the UAVDark135, UAVDT, and DTB70 datasets show that the algorithm in this paper outperforms the state-of-the-art tracking methods and has better tracking performance under light changes and fast motion (FM) scenarios. The tracking speed on a single Central processing unit (CPU) reaches 49 FPS, which satisfies the real-time requirement of UAV tracking.

A low-light image enhancement method for personnel safety monitoring in underground coal mines

Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

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.

Enhancing the Visual Effectiveness of Overexposed and Underexposed Images in Power Marketing Field Operations Using Gray Scale Logarithmic Transformation and Histogram Equalization

Abstract In this paper, we propose an adaptive gamma transform that adjusts the local values of bright and dark parts to enhance the effect of low-illumination images, thereby improving the light component. We then apply diff texture enhancement to enhance the contrast of images processed by the Retinex algorithm, thereby optimizing the perception of overexposed and underexposed imagery. Analyze the effect of image brightness enhancement based on a nonlinear transformation combined with the LOL dataset. Use PSNR and SSIM image quality evaluation criteria to analyze the visual effect of improving low-illumination images based on Retinex theory. Create a dataset of power marketing field operation inspection images and examine the effects of overexposure and underexposure image processing on four types of images: high-voltage towers, transmission lines, high-voltage fixtures, and high-voltage wireframes, using the low-light image texture fusion algorithm based on Retinex theory. Overall, this paper’s algorithm and the three DeblurGAN and DMCNN models achieve the effect of deblurring overexposed and underexposed power marketing field operation inspection images. From the local details, the model in this paper has a better effect on the de-exposure of the image, which can provide effective help for the electric power staff to understand the situation of the electric power marketing operation site and has strong practicality.

Low Light Image Enhancement in License Plate Recognition using URetinex-Net and TRBA

The license plate recognition system currently in use is susceptible to interference from the external environment and performs poorly in low-light conditions. This paper presents a solution for license plate recognition under a low-light environment. We adopted URetinex-Net methods that unfold an optimization issue into a learnable network to decompose a low illumination image into reflectance and illumination layers. We also adopted TRBA, an end-to-end recognition method involving no character segmentation. The experimental results show that the accuracy of the night environment of the proposed method is 80.11% increased by 5.11% compared to without the low light image enhancement method.

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.

A low-light image enhancement method based on HSV space

ABSTRACT To enhance the visual performance of low-illumination images, many low-illumination images are analyzed. Based on this, a low-light image enhancement method based on HSV space and semi-implicit ROF model is proposed. First, the low-illumination image is decomposed into HSV space for saturation denoising and brightness enhancement. Then, the Bayesian rules are applied to fuse the saturation and value. The three components in HSV space are converted to the RGB space and obtain a rough enhanced image. Finally, the semi-implicit ROF model is introduced to denoise the global noise and obtain the enhanced image. Such a comprehensive method can improve the low illumination image more clearly. The experimental results show that the algorithm has a PSNR score of 26.48, 6.29, 0.8947, and 28.4124, and the PSNR score is the highest in the comparison algorithm. The experiments on the Low-Light image data set also show that the proposed method can effectively improve the visibility of low-light images, and can provide a simple and effective method for low-light image enhancement.

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-illumination image contrast enhancement using adaptive gamma correction and deep learning model for person identification and verification

Low-illumination image contrast enhancement using adaptive gamma correction and deep learning model for person identification and verification

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.