[PDF] Top 20 Image Super-Resolution Reconstruction Based On Multi-Dictionary Learning

... the image, the PSNR/SSIM results has greater improvement than the Bicubic ...test image itself to guide the multi-dictionary learning is able to provide high-res images with best ... See full document

... of super-resolution reconstructed images [7], but with the increase of network depth, the gra- dient disappearance or gradient explosion problem is likely to appear in model ... See full document

... Image super-resolution creates an enhanced high resolution image using multiple low-resolution images of the same ...typical image formation model introduces major three ... See full document

... MRI super-resolution problem using the deep CNN ...using multi-scale analyses and residual networks [15, 33]; we fused multi-scale information and propagated this information along the ... See full document

... is based on single-image super resolution, which represents the sparse signal ...that image patches can be well represented as a sparse linear combination of elements from an ... See full document

... (DCNN) based on deep learning framework CAFFE and GPU which has the strong computing ...the super-resolution(SR) algorithm based on deep learning and the illumination ... See full document

... a super- resolved version of a LR textual image using sparse representation that suggest image patches from suitable dictionary ...dictionaries based clustered sparse coding approach ... See full document

... which dictionary pair among the nine clusters will give the least sparse representation error and hence the best HR patch ...directional dictionary pair and its mapping function. For patch-based ... See full document

... SR image reconstruction is normally an ill posed ...regularization based SR image reconstruction method on the basis of multi-frame image ...HR image estimates, and ... See full document

... an image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image ...prior. Based on this prior, they propose a gradient field ... See full document

... classical multi-image super- resolution, and (ii) Example-Based ...classical multi-image SR a set of low-resolution images of the same scene are ...low ... See full document

... the results obtained through bicubic interpolation. The levels of detail in the simulated rods also improve with the application of our method, Figs. 14 and 16 show a gain of counts. Fig. 14 shows the rise in the counts ... See full document

... lower resolution of the input image is first computed and inpainted using a K-NN (K Nearest Neighbours) examplar-based ...low- resolution and high-resolution patches are first learnt ... See full document

... multiple image learning based super- resolution method [3] and compare it with the existing ...the super-resolution algorithms by reducing the computational time and cost ... See full document

... a image SR reconstruction framework using multisurface ...the image details such as image gradients, curvatures, or even higher order ...original image is interpolated with half of the ... See full document

... SR image reconstruction problem as a deblurring problem with a new robust morphologic regularization ...function based on multi scale opening and closing, which could remove noise efficiently ... See full document

... handle image super-resolution work over the last few ...of super-resolution, a sparse-coding algorithm was proposed, in which a natural image can be represented by several ... See full document

... radioisotope based techniques, such as positron emission tomography (PET) which measures regional cerebral blood flow (rCBF), were widely used for mapping the brain ...for image analysis The major steps of ... See full document

... and dictionary size of 400 ato ...successful reconstruction as shown in ...and reconstruction took approximately 2 seconds on a ...patch- based reconstruction algorith m produces ... See full document

... In the past several years, sparsity has been emerg- ing as one of the most significant properties of natural images [9]. The sparsity prior suggests that image patch can be well-represented as a sparse linear ... See full document