An adaptive color plane interpolation method based on edge detection

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional （1D） gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.     

Adaptive multiscale edge detection based on vision feature of edge and wavelet transform

A new rnultiscale edge detection method is presented, which is based on an effective edge measure. The effective edge measure, used to adaptively adjust the scales of wavelet transform, is defined using the novel features of image edge obtained from human being vision characteristics. Finally, two experiments show that the proposed algorithm appears to work well.     

自适应SAR图像边缘检测算法

边缘检测是图像分析的基础,在对SAR图像进行边缘检测时,由于SAR图像存在很强的相干乘性斑点噪声,几乎没有一种方法既能有效地检测边缘又能排除斑点噪声的影响而不产生较多的虚假边缘,特别是在低视数SAR的情况下,该文指出了在低视数情况下应当如何对Touzi ratio边缘检测方法和最大似然(ML)边缘方法的检测窗口进行改进,在对SAR图像进行边缘检测时,引入了自适应窗口的方法,并将其应用到Touzi ratio边缘检测和最大似然 (ML)两个恒虚警边缘检测算法中,取得了很好的改进效果,引入自适应窗口的方法也适用于其它的SAR图像边缘检测算法。     

OSED: Object-specific edge detection

Object-specific edge detection (OSED) aims to detect object edges in an image along with classify the edge into object or non-object. It prunes edges which are not belonging to the object class for following processing, such as, feature matching for object detection, localization and three-dimensional reconstruction. In this paper, an OSED method that combines region proposal detectors with deep supervision nets to identify object-specific edges is proposed. It minimizes errors of object proposal by learning from hidden layers. Additionally, it combines features from different scales to detect object edges. In order to evaluate the performance of the OSED, we present two datasets which are captured in real scenes. The OSED method demonstrates a high accuracy of 90% and a high speed of 0.5 s for an image whose size is 512 × 448 pixels on the proposed datasets.     

一种新边缘检测算子正弦算子

边缘检测是在图像中标注灰度强度变化的过程,边缘检测的主要目的是检测和标注显著的灰度强度变化,检测准则、定位准则和响应的唯一性是评估边缘检测算子的三个准则,该文基于这三个准则,给出了获取最优边缘检测算子的详细理论推导,以一维阶跃边缘最优检测算子为例,讨论了对任意形状边缘设计边缘检测算子的过程。在此基础上,该文提出了一种新检测算子正弦算子,根据评估边缘检测算子的三个准则,该文建议的正弦算子要优于高斯一阶导函数,实验结果表明,正弦算子对检测和定位一维和二维含噪边缘都具有较好的效果。     

MULTI—GRAY EDGE DETECTION BASED ON GENERALIZED FUZZY SET

In this letter,drawbacks of the classical algorithm to enhance the fuzzy contrast among adjacent regions are analyzed.Based on it ,a new fuzzy enhancement algorithm and a linear fuzzy distribution that maps the gray images to corresponding generalized fuzzy set are proposed.Results of two examples illustrate that the algorithm is more effective and faster when used to detect the multi-level edges of images.     

基于边缘保持滤波的Canny彩色图像边缘检测方法

提出了基于边缘保持滤波的Canny彩色图像边缘检测方法。该方法利用了HSV颜色空间信道相关性低的优点,结合Canny算子定位准确的优点和边缘保持滤波理论,用边缘保持滤波取代传统的高斯滤波,用梯度矢量计算法替代传统的梯度标量计算法,从而增强了在平滑过程中对图像边缘的保持,最大程度保留了色彩的差异信息,实现了彩色图像边缘的自适应提取。实验结果证明,该方法将灰度空间的Canny算法推广到彩色矢量空间,充分利用了彩色信息,对彩色图像边缘提取具有较好的检测精度和准确度。     

基于标准差梯度的模糊边缘检测算法

红外图像的边缘检测是图像处理领域的难题之一。结合红外图像的特点,将最小误差原理推广到模糊域进而应用到红外图像的边缘检测上,提出了一种基于标准差梯度的红外图像模糊边缘检测算法。首先提出了一种基于标准差的梯度算子,将图像中潜在的边缘区域很好地区分出来;而后引入模糊最小误差阈值算法．根据此算法自适应提取了标准差梯度图像中的最优阈值,从而实现了红外图像的目标边缘检测。与传统的基于梯度的红外图像边缘检测算法进行对比实验,结果表明,该算法用于红外图像边缘检测能获得更好的效果。     

一种基于边沿检测的图像自动白平衡方法

提出了一种新的图像自动白平衡方法.首先,对图像进行直方图均衡化;然后对直方均衡化后的图像Y分量作边沿检测,在边沿包含的所有区域中选择Cb和Cr分量均值小且Y分量均值较大的区域作为参考白点区域;最后,以参考白点区域计算图像R、G和B的修正系数.使用System C对该算法进行模拟验证的结果表明,由于参考白点较准确确定,对较高色温和较低色温光源照射造成颜色偏移的图像自动白平衡较其他算法有明显改进.     

A multi-scale morphological approach to SAR image edge detection

This paper introduces a multi-scale morphological edge detection algorithm to extract SAR image edge which suffers seriously from noise. Combining the basic theme of morphology with that of multi-scale analysis, the algorithm presents the outstanding characteristics of accuracy and robustness. Comparative Experiments reveal its fine performance.     

基于BP神经网络的彩色图像边缘检测方法研究

本文主要研究了基于动量梯度下降算法的BP神经网络在彩色图像边缘检测中的应用,减少了样本训练的迭代次数,缩短了样本训练时间。同时开发了相应图形用户界面（GUI）,实现了将本文的算法与传统边缘检测算法的检测效果对比,显示不同算法的图像边缘检测效果,以及保存处理完成的图片等功能。     

基于BP神经网络的彩色图像边缘检测方法研究

本文主要研究了基于动量梯度下降算法的BP神经网络在彩色图像边缘检测中的应用,减少了样本训练的迭代次数,缩短了样本训练时间。同时开发了相应图形用户界面（GUI）,实现了将本文的算法与传统边缘检测算法的检测效果对比,显示不同算法的图像边缘检测效果,以及保存处理完成的图片等功能。     

基于边缘检测的车牌识别算法的实现

针对传统车牌识别的不足,本文提出了基于边缘检测的车牌识别的算法.该算法首先对摄像头获取的车牌图像预处理,去除图像无用信息,然后运用Robert算子检测车牌边缘,并对车牌区域进行图像较正,用高斯滤波法去除噪声并且提取车牌信息特征,接着对车牌区域水平和竖直方向运用触点定位法分割字符,对车牌分割后与相应字符模版匹配,利用预测模型预测识别结果,最后识别出车牌字符.     

Mixed scheme for image edge detection based on Wavelet Transform

A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.     

基于边缘强度的红外图像阈值分割方法研究

提出一种针对复杂场景的基于图像边缘强度的阈值分割算法。首先进行边缘检测,然后根据边缘强度进行像素灰度值加权平均计算出图像的分割阈值。该算法简单、实用,可对海上及空中目标进行准确分割,并已在实际的跟踪系统中进行了应用,结果表明目标分割正确,系统实时性强、稳定可靠。     

基于FPGA的实时彩色图像边缘检测算法的实现

针对传统的基于灰度图像的边缘检测算法抗噪能力弱、对方向敏感、获取边缘细节信息较粗等不足,本文通过分析彩色图像的特点,提出一种改进的Sobel算子与快速中值滤波相结合的彩色图像边缘检测算法,通过扩展边缘检测算子的方向模板,提高Sobel算子对纹理复杂图像的适应能力及抵抗噪声的能力。该改进算法在Altera DE2-70FPGA硬件开发平台上,应用Verilog HDL语言与Quartus II中的可编程宏功能模块实现。实验结果表明,该算法的处理只占用了约2%的系统硬件资源,资源占用相对合理,且图像边缘定位准确,抗噪能力强,能够实时有效地提取出彩色图像的边缘。     

A new edge detection algorithm for image corrupted by White-Gaussian noise

Because corruption of image by White-Gaussian noise is a frequently encountered problem in acquisition, transmission and processing of image, and classical edge detection operators such as Roberts, Sobel, Prewitt and LOG operator have the deficiency of being sensitive to White-Gaussian noise, this paper proposes a new edge detection algorithm for Image corrupted by White-Gaussian noise that can reasonably consider White-Gaussian noise reduction and correct location of edge, and provides its specific arithmetic process. Finally, the comparison based on principle of new edge detection algorithm and classical edge detection operator is done, the experimental results indicate that the performance of new edge detection algorithm is better than that of classical edge detection operator.     

基于Sobel边缘检测和双线性算法的图像缩放实现

针对传统图像缩放算法的不足,本文提出一种边缘检测和双线性插值结合的方案,用以实现图像缩放的功能。借助FPGA并行高速处理的优势,搭建各功能模块,最后通过仿真工具和板级验证,结果表明该设计能够有较理想的图像放大效果。     

SRI-Net: Similarity retrieval-based inference network for light field salient object detection

The cutting-edge RGB saliency models are prone to fail for some complex scenes, while RGB-D saliency models are often affected by inaccurate depth maps. Fortunately, light field images can provide a sufficient spatial layout depiction of 3D scenes. Therefore, this paper focuses on salient object detection of light field images, where a Similarity Retrieval-based Inference Network (SRI-Net) is proposed. Due to various focus points, not all focal slices extracted from light field images are beneficial for salient object detection, thus, the key point of our model lies in that we attempt to select the most valuable focal slice, which can contribute more complementary information for the RGB image. Specifically, firstly, we design a focal slice retrieval module (FSRM) to choose an appropriate focal slice by measuring the foreground similarity between the focal slice and RGB image. Secondly, in order to combine the original RGB image and the selected focal slice, we design a U-shaped saliency inference module (SIM), where the two-stream encoder is used to extract multi-level features, and the decoder is employed to aggregate multi-level deep features. Extensive experiments are conducted on two widely used light field datasets, and the results firmly demonstrate the superiority and effectiveness of the proposed SRI-Net.