YUV空间中基于稀疏自动编码器的无监督特征学习

现有无监督特征学习算法通常在RGB色彩空间进行特征提取,而图像和视频压缩编码标准则广泛采用YUV色彩空间。为了利用人类视觉特性和避免色彩空间转换所消耗的计算量,该文提出一种基于稀疏自动编码器在YUV色彩空间进行无监督特征学习的方法。首先在YUV空间随机采集图像子块并进行白化处理,然后利用稀疏自动编码器进行无监督局部特征学习。在预处理阶段,针对YUV空间亮度和色度通道相互独立的特性,提出一种将亮度和色度进行分离的白化措施。最后用学习到的局部特征在大尺寸图像上进行卷积操作从而获得全局特征,并送入图像分类系统进行性能测试。实验结果表明:只要对亮度分量进行适当的白化处理,在YUV空间中的无监督特征学习就能够获得相当于甚至优于RGB空间的彩色图像分类性能。     

Novel peer group filtering method based on the CIELab color space for impulse noise reduction

This paper presents a novel peer group filtering method for impulsive noise reduction. The main contributions of the proposed method are twofold. First, noise detection is performed in the CIELab, instead of the RGB, color space to enhance the noise detection effect. Secondly, two different-sized windows are used to determine the peer group for deducing more accurate status of each pixel, alleviating the problem of deducing non-corrupted pixels as corrupted in the neighborhood of edges in the textural regions. Based on five typical test color images, experimental results demonstrate that the proposed method achieves better performance in noise detection and hence noise reduction when compared to five existing competitive methods.     

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K, where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K. The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.     

UIEC^2-Net: CNN-based underwater image enhancement using two color space

Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the past few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first one to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show that the proposed method has good performance in both subjective comparisons and objective metrics. The code is available at https://github.com/BIGWangYuDong/UWEnhancement.     

基于粗糙度测量和颜色距离的织物缺陷检测方法

针对周期性纹理背景影响织物缺陷检测效果的问题,提出了一种基于粗糙度测量和颜色距离的织物缺陷检测方法。该方法先将待检测图像由RGB颜色空间转换到HSV颜色空间,并分别对三通道进行同态滤波处理,以提升缺陷与背景之间的对比度;利用粗糙度测量对织物图像进行分类,并将同一类别的织物图像分成大小相同且互不重叠的图像分块,分别估计各个图像分块与其八邻域图像分块的颜色距离,从而实现对缺陷的粗定位;最后对粗定位图像分块进行显著性和二值化处理,有效减少了周期性纹理背景对检测结果的影响。实验结果表明:与近期4种方法相比,本文方法对周期性纹理织物图像表现出了较好的检测效果,检测准确率更高。     

一种基于RGB彩色空间的影像阴影检测方法

从色彩理论和高斯-拉普拉斯算子的基本原理出发，提出了一种基于RGB彩色空间的影像阴影检测方法。实现结果表明该方法对彩色航空影像上阴影区域的检测是有效的。     

Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection

The quaternion representation (QR) used in current quaternion-based color image processing creates redundancy when representing a color image of three components by a quaternion matrix having four components. In this paper, both RGB and depth (RGB-D) information are considered to improve QR for efficiently representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. Using this improved QR, firstly we define the new quaternion pseudo-Zernike moments (NQPZMs) and then propose an efficient computational algorithm for NQPZMs through the conventional pseudo-Zernike moments (PZMs). Finally, we propose an algorithm for color image splicing detection based on the NQPZMs and the quaternion back-propagation neural network (QBPNN). Experimental results on four public datasets (DVMM, CASIA v1.0 and v2.0, Wild Web) demonstrate that the proposed splicing detection algorithm can achieve almost 100% accuracy with the appropriate feature dimensionality and outperforms 14 existing algorithms. Moreover, the comparison of six color spaces (RGB, HSI, HSV, YCbCr, YUV, and YIQ) shows that the proposed algorithm using YCbCr color space has the overall best performance in splicing detection.     

基于RGB空间的彩色图像分层检测方法研究

针对彩色图像具有颜色信息的特殊性,本文利用了背景与目标在不同颜色层亮度的差异性,提出一种对彩色图像进行RGB分层处理的目标检测方法。即通过RGB三原色原理对彩色图像分层处理,获得3幅目标图像,结合数学形态学的非线性应用,设计了自适应的阈值分割法分别对R、G、B空间进行目标检测,得到侧重点不同的检测结果,然后将检测结果进行融合。实验证明,与灰度化处理后进行目标检测方法相比,本算法在完整性和准确性方面有所提高。     

VI-NET: A hybrid deep convolutional neural network using VGG and inception V3 model for copy-move forgery classification

Nowadays, various image editing tools are available that can be utilized for manipulating the original images; here copy-move forgery is most common forgery. In copy-move forgery, some part of the original image is copied and pasted into the same image at some other location. However, Artificial Intelligence (AI) based approaches can extract manipulated features easily. In this study, a deep learning-based method is proposed to classify the copy-move forged images. For classifying the forged images, a deep learning (DL) based hybrid model is presented named as VI-NET using fusion of two DL architectures, i.e., VGG16 and Inception V3. Further, output of two models is concatenated and connected with two additional convolutional layers. Cross-validation protocols, K10 (90 % training, 10 % testing), K5 (80 % training, 20 % testing), and K2 (50 % training, 50 % testing) are applied on the COMOFOD dataset. Moreover, the performance of VI-NET is compared with transfer learning and machine learning models using evaluation metrics such as accuracy, precision, recall, F1 score, etc. Proposed hybrid model performed better than other approaches with classification accuracy of 99 ± 0.2 % in comparison to accuracy of 95 ± 4 % (Inception V3), 93 ± 5 % (MobileNet), 59 ± 8 % (VGG16), 60 ± 1 % (Decision tree), 87 ± 1 % (KNN), 54 ± 1 % (Naïve Bayes) and 65 ± 1 % (random forest) under K10 protocol. Similarly, results are evaluated based on K2 and K5 validation protocols. It is experimentally observed that the proposed model performance is better than existing standard and customized deep learning architectures.     

Feature Fusion for the Detection of Microsleep Events

A combination of linear and nonlinear methods for feature fusion is introduced and the performance of this methodology is illustrated on a real-world problem: the detection of sudden and non-anticipated lapses of attention in car drivers due to drowsiness. To achieve this, signals coming from heterogeneous sources are processed, namely the brain electric activity, variation in the pupil size, and eye and eyelid movements. For all the signals considered, the features are extracted both in the spectral domain and in state space. Linear features are obtained by the modified periodogram, whereas the nonlinear features are based on the recently introduced method of delay vector variance (DVV). The decision process based on such fused features is achieved by support vector machines (SVM) and learning vector quantization (LVQ) neural networks. For the latter also methods of metrics adaptation in the input space are applied. The parameters of all utilized algorithms are optimized empirically in order to gain maximal classification accuracy. It is also shown that metrics adaptation by weighting the input features can improve the classification accuracy, but only to a limited extent. Limited improvements are also obtained when fusing features of selected signals, but highest improvements are gained by fusion of features of all available signals. In this case test errors are reduced down to 9% in the mean, which clearly illustrates the potential of our methodology to establish a reference standard of drowsiness and microsleep detection devices for future online driver monitoring.

Low-light image enhancement based on Retinex theory and dual-tree complex wavelet transform

In order to enhance the contrast of low-light images and reduce noise in them, we propose an image enhancement method based on Retinex theory and dual-tree complex wavelet transform (DT-CWT). The method first converts an image from the RGB color space to the HSV color space and decomposes the V-channel by dual-tree complex wavelet transform. Next, an improved local adaptive tone mapping method is applied to process the low frequency components of the image, and a soft threshold denoising algorithm is used to denoise the high frequency components of the image. Then, the V-channel is rebuilt and the contrast is adjusted using white balance method. Finally, the processed image is converted back into the RGB color space as the enhanced result. Experimental results show that the proposed method can effectively improve the performance in terms of contrast enhancement, noise reduction and color reproduction.     

一种基于RGB颜色空间和随机矩形区域的显著性检测方法

提出了一种基于RGB颜色空间和随机矩形区域的显著性检测方法.该方法以R、G、B作为图像特征,然后随机产生不同位置和大小的矩形区域,并统计每个矩形区域内各像素特征值与该区域的特征均值之间的距离,再综合所有矩形区域和所有特征得到最终的显著图.因不需进行颜色空间转换,可大幅减少计算时间;同时,RGB颜色空间三通道的亮度变化比较一致,使得在特征融合时能够充分利用所有特征的信息,因而取得了更好的检测效果.实验结果表明该方法能更快速、更有效地检测出图像中的显著性区域.     

基于视觉特性的非线性多尺度彩色图像增强

[目的]针对图像在低光照下的亮度和对比度偏低的问题,提出一种基于视觉特性的非线性多尺度彩色图像增强算法.[方法]该算法将彩色图像从RGB色彩空间转化到HSI色彩空间,保持H分量不变,对S分量进行指数拉伸,对Ⅰ分量利用视觉系统模型和非线性映射方法实现图像对比度增强,再通过自适应的亮度调整增加图像的全局亮度.最后将HSI色彩空间转化到RGB色彩空间,从而实现对彩色图像自适应增强.[结果]通过对低光照彩色图像进行增强测试,其测试结果表明,[结论]该算法能够自适应地调整图像的全局亮度,增加图像的局部细节对比度,并保持其原色彩,提升彩色图像在低光照下的视见度.     

基于解耦空间异常检测的人脸活体检测算法

现有基于异常检测的方法大多仅利用活体样本进行单类建模,这样的特征用于活体检测的泛化能力强但准确率不高。而且,活体人脸特征单类建模并没有考虑活体人脸样本的多样性。活体人脸样本的不同身份、环境、采集设备等因素都会导致活体人脸的特征表达不紧凑,这样使得假体样本特征容易混入其中。为了解决以上两个问题,本文提出了一种基于解耦空间异常检测的人脸活体检测算法。本文设计了单中心对比损失,使得活体人脸特征在不限制假体人脸特征分布的情况下表达地更加紧凑。本文还对活体人脸进行了特征解耦,将其特征分为两个子空间：活体检测特征空间、活体无关特征空间。活体检测特征空间不受其他无关因素的影响,结合单中心对比损失来提高模型的泛化能力。库内实验和跨库实验共在5个数据集上与最新的方法进行了比较,在OULU-NPU数据集中,协议1相比于性能第2的模型错误率下降超过一半,最具挑战的协议4取得了仅3.3%的错误率;在SiW数据集的三个协议中也取得更低的错误检测率;在跨库实验中本文算法也表现出不错的泛化能力,尤其是在从重放攻击和打印攻击跨到3D面具攻击的跨攻击类型的测试中相比于性能第2的模型错误率下降5.41%。本文提出的人脸活...     

A hybrid color and frequency features method for face recognition

This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQ color space, which combines the R component image of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.     

基于多尺度轮廓增强的RGB-IR双波段图像语义分割算法北大核心CSCD

语义分割被广泛应用于机器人、医学成像和自动驾驶等领域,但当前语义分割主要针对可见光图像。可见光图像在光照不足或天气差的情况下成像效果较差,而红外图像受光照影响较小。因此,将可见光图像和红外图像联合使用可以有效提升模型的鲁棒性。本文针对可见光/红外(RGB-IR)双波段图像语义分割任务中目标轮廓预测不准确的问题,提出一种基于多尺度轮廓增强的双波段语义分割算法。首先,本文提出一种新的位置和通道注意力模块EEFM,基于该模块可以高效地对多个尺度的融合特征分别进行轮廓预测。其次,本文将多尺度的预测结果用于对轮廓特征进行由高分辨率至低分辨率的逐步增强。最后,本文还提出了一种新的位置和通道注意力模块SAC对融合图像特征进行增强,以最终获得更准确的分割结果。实验在一个公开RGB-IR数据集以及一个自建数据集上进行,本文所提出的模型使用较小的参数量在公开数据库上取得了57.2的分割精度,综合性能达到了最高水平。同时,消融实验也验证了所提出的各模块的有效性。     

Retinex based low-light image enhancement using guided filtering and variational framework

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization (CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.     

基于一种动态颜色模型的模糊C-均值图像分割

不同颜色的可见光本质上是具有不同波长范围的电磁波.本文试探性地提出了一种动态颜色模型,它模拟了成像曝光时间内图像平面所接收到的电磁波的动态变化.离散化之后,彩色图像的颜色特征能够被表示成一个K维矢量,称为彩色图像的动态颜色空间表示.然后建立了模糊C-均值分割算法,分别在动态颜色空间和RGB空间分割彩色图像,实验结果表明动态颜色空间的分割结果优于RGB空间的分割,从而验证了动态颜色空间的性能.笔者相信本文所提出的动态颜色模型也能够被用于纹理分析或其它的图像处理领域.     

MCR-YOLO model for underwater target detection based on multi-color spatial features

Within the fields of underwater robotics and ocean information processing, computer vision-based underwater target detection is an important area of research. Underwater target detection is made more difficult by a number of problems with underwater imagery, such as low contrast, color distortion, fuzzy texture features, and noise interference, which are caused by the limitations of the unique underwater imaging environment. In order to solve the above challenges, this paper proposes a multi-col...     

一种基于小波变换和HSI的改进视频光流场估计算法

为了避免传统基于RGB颜色模型方法在方程组中各等式线性相关和存在孔径的问题,提出一种在HSI颜色模型下结合小波变换的方法计算彩色图像视频序列光流场的计算方法,这里将传统的灰度图像光流估计方法与HSI颜色模型估计方法相结合,并且用小波变换方法对光流矢量场的异常数据点或因为匹配错误而产生的异常块数据进行剔除,从而有效提高光流场估计精度,得到精密的彩色图像光流矢量场特征。