A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

RF-Net: Unsupervised Low-Light Image Enhancement Based on Retinex and Exposure Fusion

Low-light image enhancement methods have limitations in addressing issues such as color distortion,lack of vibrancy,and uneven light distribution and often require paired training data.To address these issues,we propose a two-stage unsupervised low-light image enhancement algorithm called Retinex and Exposure Fusion Network(RFNet),which can overcome the problems of over-enhancement of the high dynamic range and under-enhancement of the low dynamic range in existing enhancement algorithms.This algorithm can better manage the challenges brought about by complex environments in real-world scenarios by training with unpaired low-light images and regular-light images.In the first stage,we design a multi-scale feature extraction module based on Retinex theory,capable of extracting details and structural information at different scales to generate high-quality illumination and reflection images.In the second stage,an exposure image generator is designed through the camera response mechanism function to acquire exposure images containing more dark features,and the generated images are fused with the original input images to complete the low-light image enhancement.Experiments show the effectiveness and rationality of each module designed in this paper.And the method reconstructs the details of contrast and color distribution,outperforms the current state-of-the-art methods in both qualitative and quantitative metrics,and shows excellent performance in the real world.

Underwater Image Enhancement Using Customized CLAHE and Adaptive Color Correction

Underwater images degraded due to low contrast and visibility issues.Therefore,it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images.The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available,low resolution,and blurriness in underwater images caused by the normal camera.Various researchers have proposed different solutions to overcome these problems.Dark channel prior(DCP)is one of the most used techniques which produced a better Peak Signal to Noise Ratio(PSNR)value.However,DCP has some issues such as it tends to darken images,reduce contrast,and produce halo effects.The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization(CLAHE)and the Adaptive Color Correction Method.The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology(JAMSTEC),and some images were collected from the internet.The measure of entropy(MOE),Measure of Enhancement(EME),Mean Square Error(MSE),and PSNR opted as performance measures during experiments.The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

Vision Enhancement Technology of Drivers Based on Image Fusion

The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.

COMBINING SCENE MODEL AND FUSION FOR NIGHT VIDEO ENHANCEMENT

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene in- formation and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.

A Progressive Feature Fusion-Based Manhole Cover Defect Recognition Method

Manhole cover defect recognition is of significant practical importance as it can accurately identify damaged or missing covers, enabling timely replacement and maintenance. Traditional manhole cover detection techniques primarily focus on detecting the presence of covers rather than classifying the types of defects. However, manhole cover defects exhibit small inter-class feature differences and large intra-class feature variations, which makes their recognition challenging. To improve the classification of manhole cover defect types, we propose a Progressive Dual-Branch Feature Fusion Network (PDBFFN). The baseline backbone network adopts a multi-stage hierarchical architecture design using Res-Net50 as the visual feature extractor, from which both local and global information is obtained. Additionally, a Feature Enhancement Module (FEM) and a Fusion Module (FM) are introduced to enhance the network’s ability to learn critical features. Experimental results demonstrate that our model achieves a classification accuracy of 82.6% on a manhole cover defect dataset, outperforming several state-of-the-art fine-grained image classification models.

Enhancing the Quality of Low-Light Printed Circuit Board Images through Hue, Saturation, and Value Channel Processing and Improved Multi-Scale Retinex

To address the issue of deteriorated PCB image quality in the quality inspection process due to insufficient or uneven lighting, we proposed an image enhancement fusion algorithm based on different color spaces. Firstly, an improved MSRCR method was employed for brightness enhancement of the original image. Next, the color space of the original image was transformed from RGB to HSV, followed by processing the S-channel image using bilateral filtering and contrast stretching algorithms. The V-channel image was subjected to brightness enhancement using adaptive Gamma and CLAHE algorithms. Subsequently, the processed image was transformed back to the RGB color space from HSV. Finally, the images processed by the two algorithms were fused to create a new RGB image, and color restoration was performed on the fused image. Comparative experiments with other methods indicated that the contrast of the image was optimized, texture features were more abundantly preserved, brightness levels were significantly improved, and color distortion was prevented effectively, thus enhancing the quality of low-lit PCB images.

Low‑light enhancement method with dual branch feature fusion and learnable regularized attention

Restricted by the lighting conditions,the images captured at night tend to sufer from color aberration,noise,and other unfavorable factors,making it difcult for subsequent vision-based applications.To solve this problem,we propose a two-stage size-controllable low-light enhancement method,named Dual Fusion Enhancement Net(DFEN).The whole algorithm is built on a double U-Net structure,implementing brightness adjustment and detail revision respectively.A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation.We also design a learnable regularized attention module to balance the enhancement efect on diferent regions.Besides,we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process.The proposed DFEN is tested on several low-light datasets,and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters.It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×10^(24)in an RTX 3090 GPU.

Single image defogging via multi-exposure image fusion and detail enhancement

Outdoor cameras play an important role in monitoring security and social governance.As a common weather phenomenon,haze can easily affect the quality of camera shooting,resulting in loss and distortion of image details.This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion(AMEF)algorithm.First,the foggy image is adaptively exposed,and the fused image is subsequently obtained via multiple exposures.The fusion weight is determined by the saturation,contrast,and brightness.Finally,the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image.It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information,effectively improving the defogging ability.

Enhanced Anti-tumor Effect of an IFN-γ-EGF Fusion Protein

The novel fusion proteins harbering human or mouse interferon combined with epidermalgrowth factor receptor binding domain were constructed using methods of genetic and proteinengineering. The fusion proteins were assayed to retain complete antiviral activities. The EGFreceptor binding moiety of the fusion proteins exhibited competitive binding saainst 125 I-EGFfor EGF receptbrs on A431 cells. The fusion proteins were shown to be more potent in in-hibiting the growth of cultured target carcinoma cells than interferon-y alone. Experimentaldata derived from rnouse Bl6 malignant melanoma models indicates that the weight of tumorin mice treated with IFN fusion proteins was significantly smaller than that of mice treatedwith interferon-y alone. The work here is unprecedented in the world and provides a reliableevidence to supPOrt the upcoming clinical employment of a class of interferons that specificallytarget tumor cell

Nuclear Fusion Within Extremely Dense Plasma Enhanced by Quantum Particle Waves

Quantum effects play an enhancement role in p-p chain reactions occurring within stars. Such an enhancement is quantified by a wave penetration factor that is proportional to the density of the participating fuel particles. This leads to an innovative theory for dense plasma, and its result shows good agreement with independent data derived from the solar energy output. An analysis of the first Z-pinch machine in mankind＇s history exhibiting neutron emission leads to a derived deuterium plasma beam density greater than that of water, with plasma velocities exceeding 10000 km/s. Fusion power could be achieved by the intersection of four such pinched plasma beams with powerful head-on collisions in their common focal region due to the beam and target enhanced reaction.

Underwater Diver Image Enhancement via Dual-Guided Filtering

The scattering and absorption of light propagating underwater cause the underwater images to present lowcontrast,color deviation,and loss of details,which in turn make human posture recognition challenging.To address these issues,this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method.First,the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image.Second,dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details.Four feature weight maps of the two images were then calculated,and two normalizedweightmapswere constructed formulti-scale fusion using normalization.To better preserve the obtained image details,the fusion image was histogram-stretched to obtain the final enhanced result.The experimental results validated that this method has improved the accuracy of underwater human posture recognition.

Enhancement algorithm for underwater weld seam image

It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency component was separated by Gaussian filter from origin image, and then it is processed by improved local contrast enhancement(LCE) algorithm to enhance the edge information. Secondly, the gamma transform with adaptive parameters was used to strengthen the image brightness, furthermore, contrast limited adaptive histogram equalization(CLAHE) algorithm was applied to enhance the image contrast. Finally, the two manipulated images were integrated together to obtain the desired image. Experiments on typical images were carried out, and evaluation results showed that this designed algorithm can effectively improve image contrast, highlight welding seam information. Moreover, the image average grey value was moderate, and the information entropy and average gradient were much higher than other algorithms.

A Multi-Channel Fusion Based Newborn Seizure Detection

We propose and compare two multi-channel fusion schemes to utilize the information extracted from simultaneously recorded multiple newborn electroencephalogram (EEG) channels for seizure detection. The first approach is known as the multi-channel feature fusion. It involves concatenating EEG feature vectors independently obtained from the different EEG channels to form a single feature vector. The second approach, called the multi-channel decision/classifier fusion, is achieved by combining the independent decisions of the different EEG channels to form an overall decision as to the existence of a newborn EEG seizure. The first approach suffers from the large dimensionality problem. In order to overcome this problem, three different dimensionality reduction techniques based on the sum, Fisher’s linear discriminant and symmetrical uncertainty (SU) were considered. It was found that feature fusion based on SU technique outperformed the other two techniques. It was also shown that feature fusion, which was developed on the basis that there was inter-dependence between recorded EEG channels, was superior to the independent decision fusion.

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Diagnosing gastrointestinal cancer by classical means is a hazardous procedure.Years have witnessed several computerized solutions for stomach disease detection and classification.However,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single source.This paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto Encoder.First,the Hyper-Kvasir dataset was employed to evaluate the proposed work.The research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual blocks.This process aims to improve the learning capability of deep models and lessen the number of parameters.Besides,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase.The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm.These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier.The experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected classes.At last,the proposed method obtained an improved accuracy of 93.90%on this dataset.Comparison is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.

Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.

基于特征强化U⁃Net的地震速度反演方法

基于深度神经网络的地震速度反演方法面临的挑战是:时间域地震数据与空间域模型信息间语义映射的弱对应关系导致多解性;神经网络将地震数据映射到速度模型过程中缺少有效引导,易受噪声干扰,影响反演精度。为此,提出一种基于特征强化U‑Net的地震速度反演方法。首先,通过多炮地震数据特征叠加使输入网络的地震时间序列信号与对应速度模型之间的空间关系更加明确;其次,基于多尺度特征融合的思想设计具有不同尺寸卷积核的模块,以增强网络对有效特征的学习能力;然后,利用注意力门引导网络,增强网络重点关注的特征;最后,结合瓶颈残差和预激活的思想,在网络中加入预激活瓶颈残差,避免梯度消失和网络退化。实验表明,该方法在地震速度反演方面具有更高的精度,并在抗噪声测试中效果较好,具有一定的泛化能力。

基于数据驱动的分布式光伏发电功率预测方法研究进展

从综述的角度,以分布式光伏系统为对象,分析了功率预测技术的发展情况、存在的难点以及主要影响因素,梳理了应用数据驱动方法实现功率准确预测的技术路线。再以空间相关性、历史出力功率以及气象等影响因素为切入点,梳理了光伏系统数据驱动的功率预测研究现状,分析其相应的数据增强、时空图信息以及特征融合的手段,讨论了技术的优缺点。最后给出了功率预测数据驱动方法研究方向和发展建议。