Low-light Image Enhancement Method Research Articles

Enhancing Low-Light Color Image via L 0 Regularization and Reweighted Group Sparsity

Classic Retinex model based low-light image enhancement methods ignored the interference of noise, which causes annoying artifacts. In this paper, we propose to estimate the illumination, reflectance and suppress the noise in a whole framework. Instead of using the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm to constrain the piece-wise smoothness, we utilize the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm to preserve the structure of the illumination map and remove the intensive noise. The clean reflectance is obtained via a novel group sparsity regularization to preserve the small scale details. Instead of using a zero-mean model for all sparse coefficients, we propose to adaptively estimate the mean of each coefficient according to the statistical characteristics of the image content. A re-weighting scheme is introduced to adjust how close the estimated patch is to the mean value. In addition, based on the observation that the noise levels in different color channels are different, the noise variance in each channel is estimated and updated during the model optimization process. Experimental results show that the proposed method outperforms the compared schemes in terms of both objective quality and visual quality.

Obstacle Distance Measurement Under Varying Illumination Conditions Based on Monocular Vision Using a Cable Inspection Robot

Obstacle distance measurement is one of the key technologies for autonomous navigation of high-voltage transmission line inspection robots. To address the robustness of obstacle distance measurement under varying illumination conditions, this article develops a research method that fuses image enhancement with robot monocular vision so that the robot can adapt to various levels of illumination running along the transmission line. During the inspection of high-voltage transmission lines in such an overexposed (excessively bright) environment, a specular highlight suppression method is proposed to suppress the specular reflections in an image; when scene illumination is insufficient, a robust low-light image enhancement method based on a tone mapping algorithm with weighted guided filtering is presented. Based on the monocular vision measurement principle, the error generation mechanism is analyzed through experiments, and we introduce the parameter modification mechanism. The two proposed image enhancement methods outperform other state-of-the-art enhancement algorithms in qualitative and quantitative analyses. The experimental results show that the measurement error is less than 3% for static distance measurements and less than 5% for dynamic distance measurements within 6 m. The proposed method can meet the requirements of high-accuracy positioning, real-time performance and strong robustness. This method greatly contributes to the sustainable development of inspection robots in the power industry.

Low‐light image enhancement based on Retinex decomposition and adaptive gamma correction

Low-light images suffer from poor visibility and noise. In this paper, a low-light image enhancement method based on Retinex decomposition is proposed. A pyramid network is first utilized to extract multi-scale features to improve the quality of Retinex decomposition. Then the decomposed illumination is refined via an adaptive Gamma correction network to handle non-uniform illumination, while the decomposed reflectance is refined with a lightweight network. Finally, the enhanced image is obtained by element-wise multiplication between the refined illumination and reflectance components. Quantitative and qualitative experiments demonstrate the superiority of our method over state-of-the-art image enhancement methods.

A lowlight image enhancement method learning from both paired and unpaired data by adversarial training

In recent years, deep learning has been widely used in the field of lowlight image enhancement. In this paper, we propose a novel deep-learning-based lowlight image enhancement method, which could learn from both paired and unpaired data, and perform end-to-end contrast enhancement and noise reduction simultaneously. Concretely, we first employ a multi-level content loss on paired synthesized data for training enhancer to recover detail better. Then, we propose a GAN-based domain adaptation mechanism, which applies an adversarial training strategy and a novel gamma-correction-based self-supervised content loss on abundant unpaired real data, for training the enhancer to perform better on real lowlight images. Furthermore, a Patch-GAN-based noise reduction mechanism is proposed to adversarially train the enhancer to better reduce noise in real lowlight images. Finally, we improve the enhancer by introduce attention mechanism and global feature to original U-net, make it more suitable for lowlight image enhancement task. We conduct experiments on several common datasets and the results show that our method outperforms other state-of-the-arts under a variety of image quality assessment metrics. And when applied as pre-processing module, our method can improve the classification accuracy on lowlight dataset by 1.7%, outperforming other methods too.

Low-Light Image Enhancement Based on Quasi-Symmetric Correction Functions by Fusion

Sometimes it is very difficult to obtain high-quality images because of the limitations of image-capturing devices and the environment. Gamma correction (GC) is widely used for image enhancement. However, traditional GC perhaps cannot preserve image details and may even reduce local contrast within high-illuminance regions. Therefore, we first define two couples of quasi-symmetric correction functions (QCFs) to solve these problems. Moreover, we propose a novel low-light image enhancement method based on proposed QCFs by fusion, which combines a globally-enhanced image by QCFs and a locally-enhanced image by contrast-limited adaptive histogram equalization (CLAHE). A large number of experimental results showed that our method could significantly enhance the detail and improve the contrast of low-light images. Our method also has a better performance than other state-of-the-art methods in both subjective and objective assessments.

Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

A low-light image enhancement method based on a deep symmetric encoder–decoder convolutional network (LLED-Net) is proposed in the paper. In surveillance and tactical reconnaissance, collecting visual information from a dynamic environment and accurately processing that data is critical to making the right decisions and ensuring mission success. However, due to the cost and technical limitations of camera sensors, it is difficult to capture clear images or videos in low-light conditions. In this paper, a special encoder–decoder convolution network is designed to utilize multi-scale feature maps and join jump connections to avoid gradient disappearance. In order to preserve the image texture as much as possible, by using structural similarity (SSIM) loss to train the model on the data sets with different brightness level, the model can adaptively enhance low-light images in low-light environments. The results show that the proposed algorithm provides significant improvements in quantitative comparison with RED-Net and several other representative image enhancement algorithms.

Color and white balancing in low-light image enhancement

Images acquired during night either appear too dark or either seem biased towards the color of lighting source. This paper proposes a low-light image enhancement technique that simultaneously preserves contrast and removes color saturation from an input image. Initially low and high frequencies are extracted from the low light image. Color balancing is performed on low frequencies using a sigmoid function. This is followed by chromatic balancing which is done using constrained linear least square minimization in YCbCr color space. The high frequencies are added back with respect to the ratio of enhanced and original low frequencies. The proposed method improves input contrast and object prominence while reducing artifacts as compared to state-of-art low light image enhancement methods.

Low-Light Image Enhancement With Semi-Decoupled Decomposition

Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a representative technique for this task, but they still have the following limitations. First, due to less-effective image decomposition or strong imaging noise, various artifacts can still be brought into enhanced results. Second, although the priori information can be explored to partially solve the first issue, it requires to carefully model the priori by a regularization term and usually makes the optimization process complicated. In this paper, we address these issues by proposing a novel Retinex-based low-light image enhancement method, in which the Retinex image decomposition is achieved in an efficient semi-decoupled way. Specifically, the illumination layer I is gradually estimated only with the input image S based on the proposed Gaussian Total Variation model, while the reflectance layer R is jointly estimated by S and the intermediate I. In addition, the imaging noise can be simultaneously suppressed during the estimation of R. Experimental results on several public datasets demonstrate that our method produces images with both higher visibility and better visual quality, which outperforms the state-of-the-art low-light enhancement methods in terms of several objective and subjective evaluation metrics.

BITPNet: Unsupervised Bio-Inspired Two-Path Network for Nighttime Traffic Image Enhancement

Due to the low luminance in nighttime traffic images, image features are not salient, making tasks in intelligent transportation systems such as nighttime vehicle detection challenging. Recently, convolutional neural network based methods have been developed for low-light image enhancement. Most of these methods are supervised and require high-light reference images at the same scenes. However, reference images are difficult to be obtained in nighttime traffic scenes because vehicles always move. In the early visual system the input signals are processed by two parallel visual paths in the retina: one path has small receptive fields (RFs) to process the high frequency information and another path has large RFs to deal with the low frequency information. Inspired by this, we design a novel bio-inspired two-path convolutional neural network (BITPNet) for nighttime traffic image enhancement. The high-frequency path with small convolution kernel size is designed to suppress noises and preserve the details. The low-frequency path with large convolution kernel size is used to enhance the luminance of images. Each path includes an encoder-to-decoder network followed by a new multi-level attention module to combine features of levels with different RFs. The outputs of the two paths are summed by learnt weights for generating the final image enhancement result. Several no-reference image quality metrics are utilized to design a new loss function, resulting in an unsupervised approach. The proposed BITPNet is trained on one nighttime traffic image dataset and evaluated on another nighttime dataset. Experimental results demonstrate that the proposed BITPNet outperforms several state-of-the-art low-light image enhancement methods in terms of visual quality and three no-reference image quality metrics. In addition, when the proposed BITPNet is used as pre-processing for the nighttime multi-class vehicle detection task, it achieves higher detection rate (97.18%) than other methods.

Low light image enhancement based on non‐uniform illumination prior model

Images captured under low-light conditions are often of low visibility. To improve visualisation, a novel low light image enhancement method is presented based on the non-uniform illumination prior model. First, the k -means method is used to process the value channel in the hue-saturation-value (HSV) colour space after space conversion of the input image. Then, the initial illumination of segmented scenes is estimated by an improved maximum red–green–blue method. Next, an illumination preservation method is presented to maintain the naturalness of the enhanced image. Furthermore, the non-uniform illumination prior model is proposed to enhance the textural details in the enhanced image. Fast Fourier transformation is used to accelerate the optimisation. Since an adaptive weight is assigned, the proposed method can preserve the edges and textures at the bright and edge areas. Experimental analysis shows that the results using the proposed method have less noise, better illumination, improved contrast, and satisfactory naturalness. In addition, the proposed method can provide better quality images in terms of subjective and objective assessments.

Low-Light Image Enhancement via the Absorption Light Scattering Model.

Low light often leads to poor image visibility, which can easily affect the performance of computer vision algorithms. First, this paper proposes the absorption light scattering model (ALSM), which can be used to reasonably explain the absorbed light imaging process for low-light images. In addition, the absorbing light scattering image obtained via ALSM under a sufficient and uniform illumination can reproduce hidden outlines and details from the low-light image. Then, we identify that the minimum channel of ALSM obtained above exhibits high local similarity. This similarity can be constrained by superpixels, which effectively prevent the use of gradient operations at the edges so that the noise is not amplified quickly during enhancement. Finally, by analyzing the monotonicity between the scene reflection and the atmospheric light or transmittance in ALSM, a new low-light image enhancement method is identified. We replace atmospheric light with inverted atmospheric light to reduce the contribution of atmospheric light in the imaging results. Moreover, a soft jointed mean-standard-deviation (MSD) mechanism is proposed that directly acts on the patches represented by the superpixels. The MSD can obtain a smaller transmittance than that obtained by the minimum strategy, and it can be automatically adjusted according to the information of the image. The experiments on challenging low-light images are conducted to reveal the advantages of our method compared with other powerful techniques.

Attribute-Based Fused Feature for Person Re-identification

In this paper, we propose an attribute-based fused feature which combined low-level and attribute features for person re-identification. Pedestrian parsing and different low-light image enhancement methods are adopted before feature extraction and Random Forest (RF) classifier is used as the basic classifier. Firstly, each of the image is divided into eight strips, and the strips are composed into four parts: head, up-body, low-body and bag areas. Above these four parts, the gradient-LOMO (Local Maximal Occurrence) features are extracted separately to form sub-classifiers features. The sub-classifiers of the independent and different dimensional attributes are respectively trained and then fused to generate a classifier, which describes 21-dimensional attribute features and is combined with the correction mechanism. Then, this classifier can be used to predict attributes on unmarked datasets. In this way, the time complexity of manual marking can be reduced effectively. Finally, the low-level features, which extracted from original and foreground images, are combined with attributes as the fused feature. The results of experiments on three common person re-identification datasets (VIPeR, PRID450S and GRID), indicate that our attribute-based fused feature exhibits prominently performance which outperforms the state-of-the-art methods for person re-identification.

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

Images captured in low-light conditions are prone to suffer from low visibility, which may further degrade the performance of most computational photography and computer vision applications. In this paper, we propose a low-light image degradation model derived from the atmospheric scattering model, which is simple but effective and robust. Then, we present a physically valid image prior named pure pixel ratio prior, which is a statistical regularity of extensive nature clear images. Based on the proposed model and the image prior, a corresponding low-light image enhancement method is also presented. In this method, we first segment the input image into scenes according to the brightness similarity and utilize a high-efficiency scene-based transmission estimation strategy rather than the traditional per-pixel fashion. Next, we refine the rough transmission map, by using a total variation smooth operator, and obtain the enhanced image accordingly. Experiments on a number of challenging nature low-light images verify the effectiveness and robustness of the proposed model, and the corresponding method can show its superiority over several state of the arts.

Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model.

Low-light image enhancement methods based on classic Retinex model attempt to manipulate the estimated illumination and to project it back to the corresponding reflectance. However, the model does not consider the noise, which inevitably exists in images captured in low-light conditions. In this paper, we propose the robust Retinex model, which additionally considers a noise map compared with the conventional Retinex model, to improve the performance of enhancing low-light images accompanied by intensive noise. Based on the robust Retinex model, we present an optimization function that includes novel regularization terms for the illumination and reflectance. Specifically, we use norm to constrain the piece-wise smoothness of the illumination, adopt a fidelity term for gradients of the reflectance to reveal the structure details in low-light images, and make the first attempt to estimate a noise map out of the robust Retinex model. To effectively solve the optimization problem, we provide an augmented Lagrange multiplier based alternating direction minimization algorithm without logarithmic transformation. Experimental results demonstrate the effectiveness of the proposed method in low-light image enhancement. In addition, the proposed method can be generalized to handle a series of similar problems, such as the image enhancement for underwater or remote sensing and in hazy or dusty conditions.

Low-light image enhancement using variational optimization-based retinex model

This paper presents an optimization-based low-light image enhancement method using spatially adaptive �������� -norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the ι2 -norm minimization artifacts at the low computational cost.

영역 분할과 로컬 히스토그램을 이용한 저조도 환경의 영상 향상 방법과 차량 블랙박스 융합

영역 분할과 로컬 히스토그램을 이용한 저조도 환경의 영상 향상 방법과 차량 블랙박스 융합

LIME: Low-Light Image Enhancement via Illumination Map Estimation.

When one captures images in low-light conditions, the images often suffer from low visibility. Besides degrading the visual aesthetics of images, this poor quality may also significantly degenerate the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a simple yet effective low-light image enhancement (LIME) method. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G, and B channels. Furthermore, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.