Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Single-frame image super-resolution through contourlet learning
EURASIP Journal on Applied Signal Processing
Steerable pyramid-based face hallucination
Pattern Recognition
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image Superresolution Using Support Vector Regression
IEEE Transactions on Image Processing
Recursive high-resolution reconstruction of blurred multiframe images
IEEE Transactions on Image Processing
Hi-index | 0.00 |
In this paper, we propose a learning-based super resolution approach consisting of two steps. The first step uses the kernel partial least squares (KPLS) method to implement the regression between the low-resolution (LR) and high-resolution (HR) images in the training set. With the built KPLS regression model, a primitive super-resolved image can be obtained. However, this primitive HR image loses some detailed information and does not guarantee the compatibility with the LR one. Therefore, the second step compensates the primitive HR image with a residual HR image, which is the subtraction of the original and primitive HR images. Similarly, the residual LR image is obtained from the down-sampled version of the primitive HR and original LR image. The relation of the residual LR and HR images is again modeled with KPLS. Integration of the primitive and the residual HR image will achieve the final super-resolved image. The experiments with face, vehicle plate, and natural scene images demonstrate the effectiveness of the proposed approach in terms of visual quality and selected image quality metrics.