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.

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.

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.

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.

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.

Contrast enhanced ultrasound of hepatocellular carcinoma

Sonazoid(Daiichi Sankyo,Tokyo,Japan),a secondgeneration of a lipid-stabilized suspension of a perfluorobutane gas microbubble contrast agent,has been used clinically in patients with liver tumors and for harmonic gray-scale ultrasonography(US)in Japan since January 2007.Sonazoid-enhanced US has two phases of contrast enhancement:vascular and late.In the late phase of Sonazoid-enhanced US,we scanned the whole liver using this modality at a low mechanical index(MI)without destroying the microbubbles, and this method allows detection of small viable hepatocellular carcinoma(HCC)lesions which cannot be detected by conventional US as perfusion defects in the late phase.Re-injection of Sonazoid into an HCC lesion which previously showed a perfusion defect in the late phase is useful for confirming blood flow intothe defects.High MI intermittent imaging at 2 frames per second in the late phase is also helpful in differentiation between necrosis and viable hypervascular HCC lesions.Sonazoid-enhanced US by the coded harmonic angio mode at a high MI not only allows clear observation of tumor vessels and tumor enhancement, but also permits automatic scanning with Sonazoidenhanced three dimensional(3D)US.Fusion images combining US with contrast-enhanced CT or contrastenhanced MRI have made it easy to detect typical or atypical HCC lesions.By these methods,Sonazoidenhanced US can characterize liver tumors,grade HCC lesions histologically,recognize HCC dedifferentiation, evaluate the efficacy of ablation therapy or transcatheter arterial embolization,and guide ablation therapy for unresectable HCC.This article reviews the current developments and applications of Sonazoid-enhanced US and Sonazoid-enhanced 3D US for diagnosing and treating hepatic lesions,especially HCC.

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.

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.

Multi-Sensor Image Fusion: A Survey of the State of the Art

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法

受煤矿井下粉尘、水雾和低照度环境影响,对皮带运输系统的监测图像精准识别极为困难。针对现有去尘雾方法的图像处理结果和效率欠佳的问题,提出一种基于暗亮通道分割融合的低照度环境图像去尘雾及增强方法。首先利用阈值分割结合伽马变换修正通道差,解决因低照度环境影响导致的尘雾浓度较大区域与其他区域间像素值差异不明显的问题,修正后通过引导尘雾图像做引导滤波得到更加符合实际情况的全局大气光强;然后为解决暗通道先验在尘雾浓度较大区域失效问题,引入亮通道先验进行补充,使用通道分量来辅助暗通道及亮通道透射率融合,避免因多次分割而导致的边缘像素归属问题;最后将去雾后RGB图像转至HSV空间,对亮度分量进行直方图均衡化并将均衡化前后的亮度分量进行加权融合,采用客观指标评价,选择最优聚合权值进行聚合,同时考虑去雾过程中饱和度损失和亮度分量与饱和度分量间的相关性提出饱和度自适应矫正函数,对图像饱和度进行矫正,色调分量保持不变,随后将图像转回至RGB空间,得到亮度适中、信息保留丰富和色彩鲜艳的图像;为验证所提方法的有效性,采用主观视觉、客观指标和目标检测精度及置信度进行算法对比,实验结果表明所提方法在上述4个指标上均优于被对比算法,其图像细节保留丰富,图像视觉观感更佳。

基于信息增强和掩码损失的红外与可见光图像融合方法

针对低光场景下红外与可见光融合图像中存在的细节弱化和边缘模糊等问题,提出一种基于信息增强和掩码损失的图像融合方法。首先,采用引导滤波增强可见光图像的纹理细节和红外图像的边缘梯度;其次,构建双分支特征提取网络提取不同模态图像的特征信息,并设计交互增强模块以渐进交互的方式集成不同特征分支的互补信息,增强特征的细节表示;然后,在融合阶段设计注意力引导模块从空间和通道维度上关注特征信息,提升网络对关键特征的感知能力;最后,提出一种掩码损失以指导融合网络有针对性地保留源图像信息,提升融合质量。为验证所提方法的融合性能,在MSRS、TNO和LLVIP公开数据集上与9种主流的融合算法进行实验对比。结果表明,所提方法在定性和定量评估上均优于其它对比算法,生成的融合图像具有丰富的纹理细节、清晰的显著性目标和良好的视觉感知。

多尺度融合图像去雾方法

图像去雾能够使视觉系统适应不同的天气状况.为克服传统暗通道先验方法会在物体边界区域形成光晕效应的问题,提出一种用于估计有雾图像透射率的多尺度融合算法.应用不同大小的最小值半径得到多尺度的透射率估计值,再根据局部区域像素具有类似的透射率值这一现象,对透射率图进行多尺度融合,选择小透射图区域中最亮的像素来计算大气光值,最后使用大气散射模型恢复清晰图像.分别从视觉效果和量化指标两个方面,对比所提方法与传统的基于先验和基于深度学习的去雾方法在进行图像去雾后的效果.结果发现,针对4种典型场景,采用本研究算法去雾后的重构图像能够保留更多的结构、细节和颜色信息,避免了过分增强和边缘部分的雾残留问题,视觉效果均优于对比方法;量化指标峰值信噪比和结构相似性均高于对比方法,分别为15.65和0.78.

一种基于SAM-MSFF网络的低照度目标检测方法

由于低照度图像具有对比度低、细节丢失严重、噪声大等缺点,现有的目标检测算法对低照度图像的检测效果不理想.为此,本文提出一种结合空间感知注意力机制和多尺度特征融合(Spatial-aware Attention Mechanism and Multi-Scale Feature Fusion,SAM-MSFF)的低照度目标检测方法 .该方法首先通过多尺度交互内存金字塔融合多尺度特征,增强低照度图像特征中的有效信息,并设置内存向量存储样本的特征,捕获样本之间的潜在关联性;然后,引入空间感知注意力机制获取特征在空间域的长距离上下文信息和局部信息,从而增强低照度图像中的目标特征,抑制背景信息和噪声的干扰;最后,利用多感受野增强模块扩张特征的感受野,对具有不同感受野的特征进行分组重加权计算,使检测网络根据输入的多尺度信息自适应地调整感受野的大小.在ExDark数据集上进行实验,本文方法的平均精度(mean Average Precision,mAP)达到77.04%,比现有的主流目标检测方法提高2.6%~14.34%.

基于激光图像特征提取的危险目标越界识别研究

为了提升危险目标越界激光识别准确性和效率,提出基于激光图像特征提取的危险目标越界识别研究。利用非线性直接变换方法完成激光点云反射图像转换的尺度不变特征点匹配;设计反锐化双掩模法图像增强结构,优化增强处理图像;对点云反射特征点展开沃尔什变换与融合,利用BP神经网络结构,识别越界的危险目标。实验结果表明,所提方法应用后图像特征细节信息得到增强,不存在局部曝光问题,对危险目标越界的错检率和漏检率最低,且具有较高的识别精度和识别效率。

一种多层线性融合的内窥镜图像增强算法

针对内窥镜图像中因光照不充分、不均匀而造成的细节模糊问题,提出了一种用于人体上消化道内窥镜图像对比度和亮度增强的算法。通过对自适应伽马校正亮度增强算法和有限对比度自适应直方图均衡化算法改进并进行线性融合。通过对输入图像分别进行亮度增强和对比度增强处理,最终得到线性融合增强图像。将提出的算法应用于开源数据集中的上消化道胃部组织图像,并与现有算法进行了对比,采用峰值信噪比(PSNR)、结构相似度(SSIM)和自然图像质量评价(NIQE)作为图像评价指标。实验结果表明,所提出的图像增强算法与现有算法相比,提高了图像质量,为医疗诊断提供更多的细节信息。

基于多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合

针对目前红外与可见光图像融合存在特征提取不足、融合图像目标区域不显著、细节信息缺失等问题,提出了一种多尺度对比度增强和跨维度交互注意力机制的红外与可见光图像融合方法。首先,设计了多尺度对比度增强模块,以增强目标区域强度信息利于互补信息的融合;其次,采用密集连接块进行特征提取,减少信息损失最大限度利用信息;接着,设计了一种跨维度交互注意力机制,有助于捕捉关键信息,从而提升网络性能;最后,设计了从融合图像到源图像的分解网络使融合图像包含更多的场景细节和更丰富的纹理细节。在TNO数据集上对提出的融合框架进行了评估实验,实验结果表明本文方法所得融合图像目标区域显著,细节纹理丰富,具有更优的融合性能和更强的泛化能力,主观性能和客观评价优于其他对比方法。

多尺度注意力特征增强融合的红外小目标检测新网络

为提高红外成像中小目标检测的性能,提高低空空域监管能力,提出一种基于多尺度注意力特征增强融合的红外小目标检测新网络。首先,使用Resnet34提取红外图像的多尺度特征;其次,使用多尺度空间注意力特征增强模块(MFEM)来提高特征提取能力;然后,在逐级上采样过程中使用双通道注意力特征融合模块(DFFM),融合语义信息和细节信息,以更好地保护红外小目标的特征;最后,与其他方法对比,并以地/空红外弱小飞机目标视频序列检测为例测试真实场景。结果表明:新方法与现有方法相比,交互比(IoU)、F值和漏检率(FNR)的评分均获得改进;通过多尺度注意力特征增强融合可准确地定位到目标并生成精细的分割结果;MFEM能够同时利用多尺度上下文信息和空间注意力机制来突出红外小目标;DFFM通过给不同通道特征的集合赋予权重,得到最合适的特征图进行特征融合,从而提高检测性能。

基于改进小波阈值函数和全尺度Retinex的红外图像融合增强算法

针对现有红外图像增强算法存在信噪比低、细节模糊、清晰度差等问题,本文提出基于改进小波阈值函数和全尺度Retinex的红外图像融合增强算法。首先,为克服尺度参数固定和光线散射导致红外图像退化的问题,利用大气透射率得到Retinex尺度参数的全尺度映射图,从而有效提高图像的清晰度,并将输入图像和使用全尺度Retinex处理后的输入图像作为算法的第一个输入和第二个输入。其次,为解决传统小波阈值函数在图像降噪过程中存在伪影、细节丢失等问题,设计改进小波阈值函数,通过引入尺度因子,在计算每层高频子图小波系数后,能根据该层数自适应调整尺度因子,并引入调节因子,结合指数函数,使该函数不仅能抑制高频子图噪声,还能极大程度保留细节信息。然后,使用小波图像融合的方式融合输入的高频子图和低频子图,进一步提高输出图像的纹理细节。主客观仿真结果表明,所提算法比其它对比算法具有更好的降噪和细节突出能力,并能提高红外图像的人眼视觉效果。最后,本文算法应用于红外成像模块采集的红外图像增强,效果良好,表明本文方法具有实用性。