The Study On The Sparse Representation Metod Of Images Based On Local Competition Mechanism

In the fields of machine vision and data compression, seeking for the"sparse"representation of the objective things has been the domain to which the experts and scholars devoted. Nowadays, due to the excellent characteristics of image sparse representation, numerous algorithms have been developed. As a result of the successful application of sparse representation of images in image compression, denoising, recognition, etc. an increasing number of researchers begin to attach importance to this field and form a new upsurge.For a piecewise smooth signal, wavelet provides a very simple and effective method. However, in high-dimension circumstances, conventional wavelet transform is not the optimal or the"most sparse"representation of functions. Overcomplete multiresolution transform could take full advantage of the information, which is contained in the function itself, and achieve the optimal approximation to the specific functions. Different transforms are appropriate for describing diversified characteristics of images, which provide powerful theories and methods for sparse representation of images. In addition, the development and evolution of biological visual system is closely related to the perceived external environment (natural images). Experiments have indicated that non-gaussian statistical properties of natural images have some correspondence with the sparse coding methods of neurons.Firstly, a sparse representation scheme of images which is inspired from overcomplete multiresolution transforms combined with visual characteristic, is proposed. The algorithm models simple cell receptive fields through Dual-Tree Complex Wavelets. The model also incorporates inhibition and facilitation interactions between neighboring cells to choose a number of coefficients of transformation to achieve the sparse representation of initial images by utilizing the local competition and inhibition method. The experiment results show that the proposed scheme outperforms others.Secondly, according to Marr's theory of preliminary vision, based on a new kind of complex wavelet—Marr-like wavelet, which could describe an image from multiscales, and a novel adaptive Harr-type wavelet transform—Tetrolet, we propose a new method for sparse representation of images which combines the two methods mentioned above. Numerical results show this method is capable of matching the geometrical characteristics of images and has better steerability as well as reconstruction result.