Based on TGV and Shearlet Transform, a Cartoon-Texture Approach for JPEG JPEG 2000 Decompression PROJECT TITLE : A Cartoon-Texture Approach for JPEG JPEG 2000 Decompression Based on TGV and Shearlet Transform ABSTRACT: Decompression of JPEG/JPEG 2000 images using a cartoon-texture-based decomposition approach is discussed in this study. To rebuild piecewise smooth images with structured textures well, the novel infimal convolution-type regularisation coupled with TGV and shearlet transform has the property of representing the positions and orientations of singularities, which can be understood as oscillation texture portions. An L2 cost functional is included in the model to improve the quality of the reconstructed images. The discretization of this functional can then be solved using the generic proximal primal-dual approach. Using numerical simulations, we've found that our model beats both the trainable nonlinear response diffusion and TV and TGV variational approaches in terms of texture preservation. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest A blind stereoscopic image quality assessor using segmented stacked autoencoders that considers the entire visual perception path Explicit Coherence in a Continuous Random Walk Model Image Segmentation Regularization
