搜索资源列表
Registration-method4
- 基于特征的自适应正则化配准算法,摆脱了局部极小值的困扰，得到了正确的配准结果-Feature-based adaptive regularization registration algorithm, to get rid of the problems of local minima, and get the correct registration results
V1I3_IJERTV1IS3037
- Image Segmentation is one of the significant elements in the part of image processing. It becomes most essential demanding factor while typically dealing with medical image segmentation. In this paper, proposal of our work comprises of formation
cnn_tutorial.pdf
- 本文档讨论和实现了卷积神经网络， 并且进行了延伸。非常重要的资料，对于深度学习有很重要的借鉴意义。 -This document discusses the derivation and implementation of convolutional neural networks (CNNs), followed by a few straightforward extensions. Convolutional neural networks involve many more connec
Reconstructing-Images-Corrupted
- A variational model to denoise an image corrupted by Poisson noise, Like the ROF model the model uses totalvariation regularization, which preserves edges
An-efficient-augmented-
- 基于经典的增广拉格朗日乘子法, 对求解一类带有特定结构（主要是针对凸规划）的非光滑等式约束优化问题, 我们提出、分析并测试了一个新算法. 在极小化增广拉格朗日函数的每一步迭代中, 该算法有效结合了带有非单调线性搜索的交替方向技术, 我们建立了算法的收敛性, 并用它来求解在带有全变差正则化的图像恢复问题.-Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algo
Efficient_Image_Dehazing
- Dehazing边界约束以及有效的图像上下文正规化-Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
Tikhonov
- 不适定问题的迭代Tikhonov正则化方法，介绍了Tikhonov正则化方法解决不定适问题-An iterative Tikhonov regularization method for ill posed problems is introduced, and the Tikhonov regularization method is introduced to solve the ill posed problem
Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal
- 该文章实现一种基于结构匹配的降噪算法，相比以往的方法，由较大的提高！
反演论文
- 二维导热程序，关于正则化反演的程序，求解导热反问题(Two dimensional heat conduction program, the procedure of regularization inversion, to solve inverse heat conduction problem.)