Facial Image Noise Removal Via a Trained Dictionary
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2014, Vol 9, Issue 1
Abstract
In this project we address that, sparsity has shown to be useful in source separation. In most cases, the sources are not sparse currently and needs to sparsify them using a known dictionary. The problem here is that, if the sparse domain is not available then it will be difficult to recover the source using the current algorithms. In-order to address this problem we fuse the dictionary into the source separation. We define a cost function based on the idea and propose by extending the de-nosing method and minimize it. The term sparse refers to signals or images with small number of non - zeros with respect to some representation bases. In sparse component analysis (SCA), the assumption is that the sources can be sparsely represented using a known common basis or dictionary. The existing system defines that the techniques like MCA which is used provide a noisy mixture and present the source. In the proposed technique FastICA algorithm which employs a modified Gaussian for blind source separation. The proposed non-linear function which is used to separate image mixtures and result in faster execution and in good quality image separation
Authors and Affiliations
Sudharson. D , Kavinraj. A. S , Sridhar. S , C. Dinesh Kumar
Keywords
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