Non-negative Sparse Coding Neural Network Models Based on Visual System
SHANG Li
SU Pin-gang
Abstract：Non-negative sparse coding neural network model can efficiently simulate the receptive field of neurons in the primary visual cortex V1 in primary visual system of brain and extract features of nature. Now this model has been used widely in the field of image processing. Considering some key influence factors,such as the selection of sparse prior distribution,the sparse constraint of feature matrix,the maximum representative-ness,class prior information of images and so on,several NNSC models are mainly discussed here including are models of NIG based NNSC with feedback mechanism denoted by NIG-NNSC,local-feature-based NNSC denoted by LNNSC,fisher-linear-discrimination-based NNSC denoted by FLD-NNSC,weighted-coding-based NNSC denoted by WCB-NNSC,etc. Research results testify that these extended NNSC models are applicable in the research field of image feature extraction,image denoising and image restoration.
Keywords：non-negative sparse codingneural networksparse distributionvisual systemprimary visual cortex v1feature basesimage processing
Publication Date：2014-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 2-11 )