mohu_img_enhance

基于SIFT的图像配准.最全版本。学习SIFT之必备资料。(image mosaic based on MATLAB)

梯度倒系数滤波,在小目标检测算法中,用来实现背景抑制,提高目标的信噪比.(reversing gradient filter coefficients in small target detection algorithm, which can be used for background suppression, the goal of improving the signal-to-noise ratio.)

介绍了不同非线性模型在随机共振中的应用，像饱和非线性，阈值非线性等(Describes the different nonlinear model of stochastic resonance in the application, such as nonlinear saturation threshold nonlinearity. . . .)

说明：  这是“An efficientlocalChan–Vesemodelforimagesegmentation”（简称LCV模型）的MATLAB源代码。LCV模型是非常重要局部区域活动轮廓模型，它被广泛使用于各个领域，如MRI大脑图像分割，血管图像分割，图像偏差场纠正。(This is "An efficientlocalChan-Vesemodelforimagesegmentation" (referred to as the LCV model) of the MATLAB source code. LCV model is very important to local area active contour model, which is widely used in various fields, such as MRI brain image segmentation, vessel segmentation, image bias field correction.)

说明：  细胞跟踪算法.文件中有细胞的运动模拟视频,说明文件.内容比较复杂,适合于研究生以上的研究人员揣摩.(Tracking Algorithm: Object Delineation Method Multiparticle.)

读取图片，用鼠标获取图片点坐标，输出坐标数据(Read the image, the image point coordinates obtained with the mouse, and output coordinate data)

利用ICM方法得到基于MRF场的SAR图像分割(Achieve market segmentation ICM s MRF)

图像时频变换实例，包括FFT变换、FFT逆变换、DCT变换、DCT逆变换、DWT变换、DWT逆变换、WHT变换、WHT逆变换等。(Examples of image frequency conversion, including FFT transform, FFT transform, DCT transform, DCT transform, DWT transform, DWT transform, WHT transform, inverse transform, etc..)

六轴陀螺仪校正算法，有详细的例子。本人已测试效果不错。(Six-axis gyroscope correction algorithm, a detailed example. I have test results are good.)

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。(simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.)