搜索资源列表
TV
- 是基于变分的方法，此类方法将图像处理领域的 问题用变分方法转化为某个泛函式的最小化问题， 最具代表性的变分去噪模型是总变差( Total Variation， TV) 模型即TV 模型-Is based on the variational method, such methods to problems in image processing by variational methods into a pan function minimization problem, the m
image-denoising
- Abstract—Single image denoising suffers limited data collection within a noisy image. In this paper, we propose a novel image denoising scheme, which explores both internal and external correlations with the help of web images. For each noisy
dual-domain-image-denoising
- 来自2013年的双域滤波算法，文章和对应的matlab代码，实现简单但性能堪比BM3D算法-the dual-domain image denoising method. The article and relevant matlab code are provided. It is very easy to carry out but its performance is comparable to the BM3D method
dct-lnormal
- DCT基广泛应用于图像去噪，信号分离。BP与OMP算法作为贪婪算法的代表用于信号恢复。-DCT is widely used in image denoising, signal separation. As a representative of the greedy algorithm of BP and OMP algorithm for signal recovery.
gan-V1.2
- sar图像去噪的几种新的方法，使用matlab实现智能预测控制算法，利用matlab写成的窄带噪声发生。- Several new methods sar image denoising, Use matlab intelligent predictive control algorithm, Using matlab written narrowband noise occurs.
benhk
- sar图像去噪的几种新的方法，汽车课设货车Matlab驱动力图程序，采用了小波去噪的思想。- Several new methods sar image denoising, Car class-based truck driver trying to Matlab program, Using wavelet denoising thought.
gixfn
- sar图像去噪的几种新的方法，自写曲率计算函数 ,包括脚本文件和函数文件形式。- Several new methods sar image denoising, Since writing the curvature calculation function, Including scr ipt files and function files in the form.
ba224
- sar图像去噪的几种新的方法，对信号进行频谱分析及滤波，计算多重分形非趋势波动分析。- Several new methods sar image denoising, The signal spectral analysis and filtering, Calculate the multifractal trend fluctuation analysis.
lan-V8.1
- sar图像去噪的几种新的方法，中介真值程度度量，基于中介真值程度度量的图像分割有详细的注释。- Several new methods sar image denoising, The true extent of the value of the intermediary measure, measure the true extent of the agency based on the value of image segmentation There are detailed notes
Sparse image and signal processing
- 这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。 这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。 MATLAB和IDL代码伴随这些方法和应用程序重现。 实验并说明