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sar图像处理gamma滤波器！！！！！sar图像处理gamma滤波器！！！！！sar图像处理gamma滤波器！！！！！sar图像处理gamma滤波器！！！！！(sar image filter gamma)

画椭圆和圆和直线的二值图求边缘点斜率(Draw the ellipse and the circle and the straight line of the binary image to find the edge slopeDraw the ellipse and the circle and the straight line of the binary image to find the edge slope)

说明：  二维、三维图像分割工具箱，采用基于matlab的标记分水岭算法(Two-dimensional and three-dimensional image segmentation toolbox, mark watershed algorithm based on MATLAB)

应用matlab对多通道遥感影像进行主成分变换，就出含有最大信息量的主成分(Application of PCA matlab multi-channel remote sensing image, containing the principal component of the maximum amount of information)

工业摄影测量技术，利用matlab编写数字图像处理界面，包含相关手写算法，包括canny算子，LOG算子，canny滤波，高低通滤波，butterworth高低通滤波，数米粒，全方位图展开技术……(write a GUI of digital image processing with matlab，including canny operator，LOG operator，Lowpass and highpass filter，etc)

提出一种基于空间域的多聚焦图像融合技术：首先计算点分布函数（PSF），然后利用PSF模糊源图像，最后通过组合原图像中较为清晰的像素点来生成全聚焦图像。(We propose a multi-focus image fusion based on space domain techniques: First calculate the point distribution function (PSF), then use the PSF fuzzy source image, and finally to generate omni-focus image by combining the original image clearer pixels.)

基于matlab的、结合深度图像的图像修补( Image patch based on matlab, combined with the depth of the image )

说明：  对原始图像进行阈值分割，把金属图像分割出来，抠掉金属的图像(Threshold segmentation is applied to the original image, and the metal image is segmented to remove the metal image)

针对杂波环境下的多个机动目标跟踪问题, 本文将多模型概率假设密度 (Multiple-model probability hypothesis density, MM-PHD) 滤波器和平滑算法相结合, 提出了 MM-PHD 前向 – 后向平滑器. 为了避免引入复杂的随机有限集 (Random finite set, RFS) 理论, 本文根据 PHD 的物理空间 (Physical space) 描述法推导得到了 MM-PHD 平滑器的后向更 新公式. 由于 MM-PHD 前向–后向平滑器的递推公式中包含有多个积分(By integrating the multiple-model probability hypothesis density (MM-PHD) filter with the smoothing al- gorithms, an MM-PHD forward-backward smoother is proposed in this paper for tracking multiple maneuvering targets in clutter. To avoid use of complex random finite set (RFS) theory, the backward updated equation of the MM-PHD smoother can be derived according to the physical-space explanation of the PHD)

Zhang等人 利用相位一致性信息的特性，提出了 特征相似性方法(FSIMG) ，选取了相位一致性信息和梯度信息 作为它的两个特征，得到了较好的结果。 (A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA s demonstrate that FSIM can achieve much higher consistency with the subjective uations than state-of-the-art IQA metrics.)