We benchmarked the performance of sparse deconvolution on imaging structures with known ground truth. By low-pass filtering the image obtained by the 1.4-NA objective with a synthetic PSF from a 0.8-NA objective
At the same time, during the graph-based semi-supervised learning stage, similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. To make the ISSC scale up to larger databases, a novel online dictionary ...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the b
Deep learning 说明 摘要 引言 Supervised learning Backpropagation to train multilayer architectures Convolutional neural networks Image understanding with deep convolutional networks Distributed representati...deep learning 最常见的 function set 利用并行计算(上图),GPU加速,要矩阵运算,CALL GPU来做矩阵运算 outp...
Learning A Sparse Transformer Network for Effective Image Deraining Xiang Chen1 Hao Li1 Mingqiang Li2 Jinshan Pan1* 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2Information Science Academy, China Electronics Technology Group Corpo...
Learning A Sparse Transformer Network for Effective Image Deraining Xiang Chen1 Hao Li1 Mingqiang Li2 Jinshan Pan1 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2Information Science Academy, China Electronics Technology Group Corporation Overview In this ...
In the next section, we will introduce an online DL approach that can effectively solve this large-scale learning problem. 6.2.4 Online Dictionary Learning A stochastic online learning algorithm was proposed in Mairal et al. (2009) in order to learn dictionaries for a large set of training ...
The current method is named “Sparse Similarity Learning based on Posterior distribution (SSLP)”. When compared to similar works, its other advantages are: The rest of the current paper is organized as follows: Section 2 reviews related works. In Section 3, the proposed similarity measure and ...
Sparse representation provides an efficient way for the compression of hyperspectral images in the literature. In this work, an improved version of the Spectral-Spatial Adaptive Sparse Representation (SSASR), Modified SSASR (MSSASR), is proposed for hyperspectral image compression. In the first step...
This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. 这种表示在一种新颖的pooling校正方法上探索了目标的部分信息和空间信息。 The similarity obtained by pooling across the local patches helps not only locate the target more...