Low Illumination Image Research Articles

A Multi-Scale Fusion Residual Encoder-Decoder Approach for Low Illumination Image Enhancement

In a low-light environment, due to the extremely small number of photons and high noise, the sensory light source of the line-scan camera cannot be fully exposed, resulting in a decrease in image quality. To this end, a low illumination image enhancement approach based on multi-scale fusion residual encoder- decoder is proposed, which directly learn the end-to-end mapping between the original sensor RAW light and dark images, and effectively enhance the brightness of the image while completely restoring the original image details and colors. In order to increase the feature diversity and speed up the network training, the network structure is added with residual block. To aggregate the global multi-scale features of the context, a dense context feature aggregation module is designed to make up for the lack of spatial information in the network. Based on the SID dataset, a comparative experiment with other 10 methods shows that proposed method is significantly better than most other methods in visual effects and quantitative evaluation of PSNR and SSIM. While restoring the brightness of the image, the edges and colors of the image are effectively represented, and finally a satisfactory visual quality is obtained under low light enhancement.

Technical research of composite residual network in low illumination image enhancement

Technical research of composite residual network in low illumination image enhancement

Low illumination image enhancement method based on quad-stable stochastic resonance

Low illumination image enhancement method based on quad-stable stochastic resonance

Method for Correcting Low-illumination Images Based on Adaptive Two-dimensional Gamma Function

A two-dimensional gamma function-based color image correction method based on the illumination-reflection model and multi-scale theory is proposed. Firstly, the original image is histogram equalized, then converted to HSV color space, and the light components of the scene are extracted by multi-scale Gaussian function, and then the parameters of the 2D gamma function are adjusted by the distribution characteristics of the light components to construct a 2D gamma function to enhance the image. Finally, the images are fused. The comparison with the classical algorithm shows that the algorithm in this paper can better reduce the effect of uneven illumination on the image and improve the brightness and contrast of the image.

Multi-domain Block Low-illuminance Image Enhancement Algorithm Combined with Genetic Algorithm

Multi-domain Block Low-illuminance Image Enhancement Algorithm Combined with Genetic Algorithm

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior.

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Research on Image Enhancement Algorithm Based on Artificial Intelligence

With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.

Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter

ABSTRACT In a low-illumination environment, the contrast between lane lines and the ground is relatively low. Traditional image enhancement algorithms, such as gamma correction, Histogram Equalization, and multiple-scale Retinex, may result in over enhancement and detail loss, which decreases the detection accuracy of driver assistance systems. In this work, we introduce a low-illumination image enhancement algorithm based on improved Retinex theory and apply it to lane-line detection. A luminance channel optimization method based on a bimodal energy function is adopted to select the weight of the linear combination. A guided filter with edge-preservation is applied to obtain the illumination component of the scene. Furthermore, the reflection component is estimated in a hyperbolic tangent space, and the luminance and contrast are adaptively adjusted to obtain the enhanced image. Experimental results show that proposed method can effectively extract the lane-line edges and suppress the noise in the dark area with low lane-line illumination. Moreover, it can improve the detection success rate of an assisted driving system.

A mine low illumination image enhancement algorithm

A mine low illumination image enhancement algorithm

Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN

Human recognition in indoor environments occurs both during the day and at night. During the day, human recognition encounters performance degradation owing to a blur generated when a camera captures a person’s image. However, when images are captured at night with a camera, it is difficult to obtain perfect images of a person without light, and the input images are very noisy owing to the properties of camera sensors in low-illumination environments. Studies have been conducted in the past on face recognition in low-illumination environments; however, there is lack of research on face- and body-based human recognition in very low illumination environments. To solve these problems, this study proposes a modified enlighten generative adversarial network (modified EnlightenGAN) in which a very low illumination image is converted to a normal illumination image, and the matching scores of deep convolutional neural network (CNN) features of the face and body in the converted image are combined with a score-level fusion for recognition. The two types of databases used in this study are the Dongguk face and body database version 3 (DFB-DB3) and the ChokePoint open dataset. The results of the experiment conducted using the two databases show that the human verification accuracy (equal error rate (ERR)) and identification accuracy (rank 1 genuine acceptance rate (GAR)) of the proposed method were 7.291% and 92.67% for DFB-DB3 and 10.59% and 87.78% for the ChokePoint dataset, respectively. Accordingly, the performance of the proposed method was better than the previous methods.

A night low‐illumination image enhancement model based on small probability area filtering and lossless mapping enhancement

A novel night-time image enhancement approach was proposed in this paper to address the problems of low contrast and poor details of low-illumination images captured at night. To begin with, the luminance component V was extracted that was irrelevant to the colour information of the image upon converting the image to the HSV space from the RGB space. Then, by converting the luminance component V of the image into the probability space, the image was divided into a small-probability grey-scale area and a normal area based on the theory of probability. Moreover, pixels were transferred from the small probability area to the normal area of the image according to the nearest attribution principle that was established. Lastly, the contrast enhancement of the image was realized thanks to lossless mapping functions without losing the number of grey levels of the image. As can be observed from experimental results, the proposed method is superior to the most advanced algorithm in visual quality and quantitative measurement.

An adaptive enhancement algorithm based on visual saliency for low illumination images

An adaptive enhancement algorithm based on visual saliency for low illumination images

Blind Deblurring Based on Sigmoid Function.

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.

Combined Gray Level Transformation Technique for Low Light Color Image Enhancement

Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model. Finally, the enhanced illumination image is blended with original reflectance image to get enhanced color image. This paper shows that the proposed algorithm on various weakly illuminated images is enhanced better and has taken reduced computation time than previous image processing techniques.

Enhanced network optimized generative adversarial network for image enhancement

With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

Learning-based low-illumination image enhancer for underwater live crab detection

Abstract Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab’s phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm’s robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s−1 using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs.

Multi-Exposure Fusion of Gray Images Under Low Illumination Based on Low-Rank Decomposition

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.

Low-Illumination Image Enhancement in the Space Environment Based on the DC-WGAN Algorithm.

Owing to insufficient illumination of the space station, the image information collected by the intelligent robot will be degraded, and it will not be able to accurately identify the tools required for the robot’s on-orbit maintenance. This situation increases the difficulty of the robot’s maintenance in a low-illumination environment. We proposes a novel enhancement method for images under low-illumination, namely, a deep learning algorithm based on the combination of deep convolutional and Wasserstein generative adversarial networks (DC-WGAN) in CIELAB color space. The original low-illuminance image is converted from the RGB space to the CIELAB color space which is relatively close to human vision, to accurately estimate the illumination image, and effectively reduce the effect of uneven illumination. DC-WGAN is applied to enhance the brightness component by increasing the width of the generation network to obtain more image features. Subsequently, the LAB is converted into RGB space to obtain the final enhanced image. The feasibility of the algorithm is verified by experiments on low-illuminance image under general, special, and actual conditions and comparing the experimental results with four commonly used algorithms. This study lays a technical foundation for robot target recognition and on-orbit maintenance in a space environment.

Low-light images enhancement method in coal mine based on knowledge distillation

In order to solve the problems of low illumination and poor image quality in video monitoring systems caused by complex lighting conditions in underground mines, a low-light image enhancement method based on knowledge distillation is proposed. By establishing Net-S (student network) and Net-T (teacher network) for feature simulation, this method improves the image reconstruction ability of the model while transferring knowledge and improves the network efficiency and image enhancement effect at the same time. The improved RIR (residual in residual blocks) module is added to improve the feature extraction ability of the model. At the same time, the loss function for low illumination image characteristics is designed to improve the accuracy of the network model in image reconstruction. Compared with the traditional knowledge distillation network model, Net-T and Net-S of the model output different images respectively. Net-T extracts features from high-quality images. Net-S simulates features under the guidance of Net-T, and finally obtains enhanced images. The experimental results show that the model can effectively remove part of the noise, improve the brightness and quality of the image, and improve the visual effect of the image. Moreover, the model has good generalization ability. Compared with CLAHE and LIME, the absolute cumulative change of the normalized objective index of this method is increased by 42.21%, 7.47% and 30.18% respectively. In view of the low illumination image enhancement of the mine, this method has certain advantages, which can significantly improve the overall brightness of the image, reduce the noise, and is more in line with the human visual perception. To a certain extent, it meets the needs of the construction of intelligent mine video monitoring system.