signalprocessing

表單驗證 調用為 onBlur=checkform(fname1,fname2,types,value1,value2) 輸入框的驗證 onSubmit subt(this.id) 表單提交時的驗證 兩者需要一起使用 fname1=驗證的項目名 fname2=驗證的副項目名 types=驗證的方式 value1=驗證的值1 value2=驗證的值2 驗證的方式值:(多驗證方式用"|"分割 例: 1|2|3) 0.該值不能為空! 1.您輸入太短了!不能小於value1字符 2.您輸入太多了!不能超過value2字符! 3.你的兩次密碼不一至,請驗證! 4.您的輸入與要求不相符,該輸入只能在value1~value2之間! 5.您輸入的E_mail不正確!請重新輸入! 6.只能為數字 */(calling for the certification form onBlur = checkform (fname1. fname2, types, value1, value2) input box test onSubmit subt (this.id) at the time of submission of the form Taken together need to verify the use of the test fname1 = fname2 = projects were validated by the deputy project were typ es = verifiable means value1 = the value of a certification value2 = 2 certification test the value of values : (multi-authentication "|" segmentation cases : 1 | 2 | 3) 0. The value can not be empty! 1. Your input is too short! value1 not less than two characters. You lose into too many! not more than value2 characters! 3. your password twice to different, Please validation! 4. Your input is not in line with requirements, The importation of only between value1- value2! 5. E_mail you enter )

用于脸部识别时，使用基于像素和区域的图像特征核的稀疏表示的识别算法。(When used in face recognition using image recognition algorithm based on feature pixel and regional nuclear sparse representation.)

说明：  为了在抑制噪声时尽可能保留图像边缘信息，提出一种自适应各向异性高斯滤波器设计方法. 该方法由独立 强度传播（IDS）模型决定滤波器的长轴尺度，由像素的邻域平滑度决定长短轴的比例，然后根据该处的灰度梯度方 向自适应决定各向异性滤波器的长轴方向. 仿真实验表明，提出的自适应各向异性滤波器具有很强的噪声抑制和 边缘保持能力. (In order to suppress noise, as far as possible to keep the image edge information, an adaptive anisotropic Gaussian filter design methods. The method by the independent strength spread (IDS) model determines the long axis of the filter scale, the pixel neighborhood smoothness determine the ratio of minor axis, and then there' s gray gradient direction of adaptive decision the long axis of anisotropic filter. Simulation results show that the proposed adaptive anisotropic filter has a strong noise suppression and preserve edges.)

说明：  一种基于tv模型的彩色图像修复方法，效果很好(Tv model based on color image restoration)

Image processing using sobel operator

实现了C聚类以后的马尔科夫图像分割，对阈值的选取和图像的后验概率计算(C clustering after the Markov segmentation, the posterior probability threshold value is selected and images calculated)

把一幅图像进行经验模式分解，结果为不同频率分量的IMF，用于希尔伯特黄变换，图像去噪等(empirical mode decomposition)

原始的criminisi灰度图像修复算法及实验图片(Original Criminisi grayscale image restoration algorithm and experimental images)

利用DCT字典对图形进行稀疏表示，实现两幅图像的融合(Using the DCT dictionary to sparse representation of the graphics to achieve the fusion of two images)

采用haar小波实现二维图像的分解与重构（mallat算法）：(Two-dimensional image using haar wavelet decomposition and reconstruction (mallat algorithm):)