Maize leaf disease image enhancement algorithm using TFEGAN

Self-supervised network for low-light traffic image enhancement based on deep noise and artifacts removal

In this paper, we propose to utilize generative adversarial networks (GANs) to achieve successful gaze estimation in interactive multimedia environments with low light conditions such as a digital museum or exhibition hall. The proposed approach utilizes a GAN to enhance user images captured under low-light conditions, thereby recovering missing information for gaze estimation. The recovered images are fed into the CNN architecture to estimate the direction of user gaze. The preliminary experimental results on the modified MPIIGaze dataset demonstrated an average performance improvement of 6.6 under various low light conditions, which is a promising step for further research.

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.

With the rapid development of visual sensors and artificial intelligence (AI), video/image data has increased dramatically, especially in the era of AI-enabled intelligent transportation. In low-light imaging conditions, however, the camera only captures weak scene-reflected light. The visual data is thus inevitably affected by noise, low contrast, and poor brightness, and so on. It will have a negative influence on the development of vision-based traffic situational awareness, traffic safety management, and automatic/autonomous vehicles. To guarantee high-quality visual data, the multiscale deep stacking fusion enhancer (termed MDSFE) is proposed to enhance a low-light image. In particular, our MDSFE consists of four components, i.e., coarse extraction module (C-EM), coarse attention fusion module (C-AFM), multiscale dense enhancement module (M-DEM), and fine encoder–decoder fusion module (F-EDFM). The combination of these modules is capable of enhancing the abilities of feature mapping and expression. Experimental results on both synthetic and real-world scenarios have illustrated that the proposed method can provide superior enhancement results under different imaging conditions. It also has the capacity of improving the detection precision under low-light conditions.

Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day. Although multi-task learning and contextual-information-based methods have been proposed to solve the problem, they either require additional manual annotations or introduce extra inference overhead respectively. In this paper, we propose a style-transfer-based data enhancement method, which uses Generative Adversarial Networks (GANs) to generate images in low-light conditions, that increases the environmental adaptability of the lane detector. Our solution consists of three parts: the proposed SIM-CycleGAN, light conditions style transfer and lane detection network. It does not require additional manual annotations nor extra inference overhead. We validated our methods on the lane detection benchmark CULane using ERFNet. Empirically, lane detection model trained using our method demonstrated adaptability in low-light conditions and robustness in complex scenarios. Our code for this paper will be publicly available.

Generative Adversarial Network is the topic of interest in today’s research in the field of image processing and computer vision. A basic GAN model was introduced by Ian Goodfellow et al. in 2014. After that advancement in the field of research in GAN models has been application specific. In computer vision and image to image translation GANs are playing very effective role either in the case of face detection and recognition or in image resolution enhancement and image augmentation. This paper represents a concise overview of various GAN models along with their features and applications. Pix2Pix and conditional GAN models work upon paired datasets while other models like cycle GAN, discover GAN, dual GAN, info GAN, deep convolutional GAN etc. work upon unpaired datasets. Various image datasets which are commonly used for training of generator and discriminator networks are also discussed in this paper. Since partial mode collapse is a common problem to occur during training process for all models, therefore various normalization techniques are also preferred during the training of generator and discriminator networks. As the advancements in GAN models are increasing at a very fast rate, soon these models will also be preferred in commercial applications.

In the field of speaker verification, the speaker systems based on x-vector framework are widely used in many scenarios. However, it suffers from the performance degradation caused by noise disturbance. In this paper, we firstly analyzed the noisy robustness of x-vector by training the networks using a mixture dataset which includes clean data and corrupted data. Then, we proposed a novel adversarial strategy against noise interference and extracted the noise-robust speaker embedding with x-vector. The proposed adversarial method named as triple-net GAN employs three connected networks: a generator network (G), a discriminator network (D) and a classifier network (C). The spectral coefficients of clean and noisy speech utterances are fed to the G, of which the structure is nearly the same as x-vector. The outputs of G are transferred in a parallel way to the D and C. And the labels of D are set binary for clean data and corrupted data, while the labels of C are set corresponding to speaker identities, which aims to learn the speaker embedding features invariant to the noise. Finally, we executed the experiments with different variants of triple-net GAN to verify the denoising capability of the proposed adversarial method. Experimental results on Librispeech corpus demonstrate that our proposed method could achieve a better performance under the noisy environments.

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.

Face detection in low-light conditions remains challenging due to underexposure, noise, and unstable contrast, which significantly degrade the performance of convolutional-based models. Conventional enhancement techniques, such as Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Low-Light Image Enhancement (LIME), often improve brightness but introduce visual artifacts and lack robustness for face detection. This study proposes Low-light Unified Multi-stage Enhancement Network (LUMEN), a lightweight multi-stage image enhancement pipeline designed as a preprocessing module to improve RetinaFace-based face detection under low-light conditions. LUMEN integrates Multiscale Retinex with Color Restoration, adaptive gamma correction, CLAHE-based local contrast enhancement, controlled image fusion, and Non-Local Means denoising to jointly stabilize illumination, preserve texture, and maintain visual naturalness. The method is evaluated on a low-light subset of the Human Faces Object Detection Dataset using RetinaFace. Detection performance is assessed using detection rate and confidence score, while visual quality is evaluated using no-reference metrics such as the Natural Image Quality Evaluator (NIQE) and the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Experimental results show that LUMEN achieves a face detection rate of 91% with a high confidence score (0.9545), rep-resenting an improvement of 33 percentage points over raw low-light images (58%), while maintaining detection performance comparable to CLAHE, which ranks as the second-best method, LUMEN delivers superior perceptual quality, evidenced by the lowest BRISQUE score, as well as more stable visual appearance with reduced noise amplification and fewer contrast artifacts in low-light facial regions. LUMEN also achieves the lowest BRISQUE score, indicating superior texture preservation and perceptual stability. Ablation studies confirm that Retinex and CLAHE are the most critical components for detection robustness, while gamma correction, fusion, and denoising mainly contribute to visual naturalness. These results demonstrate that LUMEN provides an effective and practical preprocessing solution for low-light face detection.

With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perform poorly against previously unseen adversarial attacks. Anomaly‐based IDSs (AIDSs) use Machine Learning (ML) and Deep Learning (DL) approaches to overcome these limitations. However, AIDS performance can be poor when trained on imbalanced datasets. To address the challenge of AIDS performance caused by these unbalanced training datasets, generative adversarial models are proposed to obtain adversarial attacks from one side and analyse their quality from another. According to extensive usage and reliability criteria for generative adversarial models in different disciplines, Generative Adversarial Networks (GANs) , Bidirectional GAN (BiGAN) , and Wasserstein GAN (WGAN) are employed to serve AIDS. The authors have extensively assessed their abilities and robustness to deliver high‐quality attacks for AIDS. AIDSs are constructed, trained, and tuned based on these models to measure their impacts. The authors have employed two datasets: NSL‐KDD and CICIDS‐2017 for generalisation purposes, where ML and DL approaches are utilised to implement AIDSs. Their results show that the WGAN model outperformed GANs and BiGAN models in binary and multiclass classifications for both datasets.

In low light condition, color (RGB) images captured by visible sensors suffer from severe noise causing loss of colors and textures. However, near infrared (NIR) images captured by NIR sensors are robust to noise even in low light condition without color. Since RGB and NIR images are complementary in low light condition, the multispectral fusion of RGB and NIR images provides a viable solution to low light imaging. In this paper, we propose multispectral fusion of RGB and NIR images using weighted least squares (WLS) and convolution neural networks (CNNs). We combine traditional WLS filtering for layer decomposition and denoising with latest deep learning for image enhancement and texture transfer into the multispectral fusion to take both advantages. We build two networks based on CNN: image enhancement network (IEN) for image enhancement and texture transfer network (TTN) for NIR texture transfer. First, we perform RGB image denoising based on WLS filtering and generate the base layer. We use both RGB and NIR images for WLS filtering as weights to filter out noise in low light RGB images. Second, we conduct IEN to enhance contrast of the base layer. Third, we perform TTN to deliver NIR details completely and naturally to the fusion result. The combination of WLS, TTN and IEN leads to noise reduction, contrast enhancement, and detail preservation in fusion. Experimental results show that the proposed method achieves good performance in both noise reduction and detail transfer as well as outperforms state-of-the-art methods in terms of visual quality and quantitative measurements.

As a promising generative modeling method, Generative Adversarial Networks are a deep-learning-based generative model, in which two networks, namely the generative network and the discriminant network, contest with each other in a game during which the generator “learns” and get trained to be able to fool the discriminator of believing a specific image is (superficially) authentic. It is widely used to generate pictures based on existing data. Based on the MNIST data set, this paper studies and analyzes the application of Generative Adversarial Networks in the generation of handwritten digital images. In addition, by studying the Deep Convolutional Generative Adversarial Network and the Conditional Generation Adversarial Network model, we compared the connections and differences between these three models in training the MNIST dataset to generate handwritten digits algorithms, and gave an optimization method for generating handwritten digits.

The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.

In low light conditions, image enhancement is critical for vision-based artificial systems since details of objects in dark regions are buried. Moreover, enhancing the low-light image without introducing too many irrelevant artifacts is important for visual tasks like motion detection. However, conventional methods always have the risk of “bad” enhancement. Nocturnal insects show remarkable visual abilities at night time, and their adaptations in light responses provide inspiration for low-light image enhancement. In this paper, we aim to adopt the neural mechanism of dark adaptation for adaptively raising intensities whilst preserving the naturalness. We propose a framework for enhancing low-light images by implementing the dark adaptation operation with proper adaptation parameters in R, G and B channels separately. Specifically, the dark adaptation in this paper consists of a series of canonical neural computations, including the power law adaptation, divisive normalization and adaptive rescaling operations. Experiments show that the proposed bio-inspired dark adaptation framework is more efficient and can better preserve the naturalness of the image compared to existing methods.

Images taken under low-light conditions often suffer from multiple degradations such as low visibility and unknown noise. Low-light image enhancement is an important task in the field of computer vision. In order to avoid the limited number of samples in paired datasets, several self-supervised enhancement methods have been developed. However, due to the designed illumination gradient prior, most self-supervised enhancement methods based on Retinex cannot effectively constrain the illumination or suppress the amplified real noise. To solve this problem, this paper explores a joint self-supervised enhancement and denoising method for low-light image. Initially, we proposed a new regularization term, named TV-Huber, and developed an adaptive illumination estimation network (AIE-Net) to explore the intrinsic relationship between structure and texture in the illumination map. Next, the camera response model and the learned illumination are then used to enhance the contrast of low-light images and mitigate color shifts. Finally, the learned illumination maps are transformed into illumination masks. Under the assumption of independent and zero-mean noise, selective feature injection is performed on the shallow features extracted by the blind-spot network (BSN) to reduce information loss while removing unknown real noise in the dark area. Extensive experiments show that the proposed method has good generalization ability on five challenging low-light image datasets and outperforms other methods in terms of visual quality and quantitative comparison.