1.6 尺度不变特征提取

图象分割程序：先用梯度函数进行边缘检测,然后二值化后分割图象(Image segmentation process: first the gradient edge detection function, and then after binarization image segmentation)

对流层与电离层中电波传播的相关问题研究,很好的文章(Troposphere and ionosphere wave propagation issues related to research)

1、参数初始化； 2、形成边界削波数据； 3、波场初始化； 4、Zmax层波场延拓到深度Zmax-1； 5、Zi+1层波场延拓到深度Zi； 6、重复5，从Iz Nz-1开始，直到Iz 1，得测线上的频率—空间域波场； 7、频率-空间域波场对频率做反傅里叶变换，得时间-空间波场； (1, parameter initialization 2, the formation of boundary clipping data 3, wave field initialization 4, Zmax layer wave field extended to the depth of Zmax-1 5, Zi+1 layer wave field extended to the depth of Zi 6, repeat 5, starting Iz Nz-1 until Iz 1, the frequency of the test line- spatial domain wave field 7, frequency space domain wave field to frequency inverse Fourier transform, time- space field )

二维码形变校正，扭曲检测，初定位,边缘检测(calibration of a distorted QR code)

两种不同量化步长的图像采样编码重够后的图像比较(Comparison of the two different images of the image sample quantization step size after encoding the heavy enough)

Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.

说明：  最二小乘影像匹配，精度可以达到0.01个像素，也可以最二小乘影像模板匹配。经过修改后可以直接用，只要调用一个函数就搞定，函数参数的说明很清楚，绝对不会有任何困难，非常好用。(Most two Theravada image matching accuracy can reach 0.01 pixel images can also be the most two Theravada template matching. After modifications can be directly used, as long as is you call a function, function parameters, the description is clear and will not have any difficulties, very easy to use.)

说明：  将图像进行各个角度的旋转，以及对两幅或多幅图像进行叠加(Each rotation angle of the image, and the two or more images are superimposed)

图像融合程序，用以提取图像的高分辨率部分，实现图像的清晰化(image fusion procedure to extract high-resolution images, the images clearer)

复数乘法实现图像旋转，并且运用插值对旋转后的图像优化(Complex multiplication of image rotation, and the use of interpolation of the rotated image optimization)