Open Access
Subscription Access
A Novel Cascaded Image Transform by Varying Energy Density to Convert an Image in to Sparse
Background: All natural signals are subjected to sparsity when they are properly represented by a basis function. Sparsity helps us to sample the signals less than Nyquist rate which clearly explained by the recent theory known as compressive sensing. Methods: This paper explains that DFT does a good job in converting the given image into sparse when the energy density of the image is varied and also a cascaded transform DFT and DWT is proposed. Qualitative measures for the cascaded transform were observed to be good. Result: It helps us to convert a given image signal into sparse without loss in information content present in that image. Application: While converting an analog signal into digital, sparsity will help to compress a given analog signal before conversion. So the number of samples obtained by sampling the compressed signal becomes less.
Keywords
Compressive Sensing, Energy Density, Image Transforms Information Preservation Capability, Sparsity.
User
Information
Abstract Views: 246
PDF Views: 0