Novel Super-Resolution Method Based on High Order Nonlocal-Means
Super-resolution without explicit sub-pixel motion estimation is a very active subject of image reconstruction containing general motion. The Non-Local Means (NLM) method is a simple image reconstruction method without explicit motion estimation. In this paper we generalize NLM method to higher orders using kernel regression can apply to super-resolution reconstruction. The performance of the generalized method is compared with other methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ReconstructionMotion EstimationregressionSuper-ResolutionVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Guided Nonlocal Means Estimation of Polarimetric Covariance for Canopy State Classification
We have developed a nonlocal algorithm for estimating polarimetric synthetic aperture radar (PolSAR) covariance matrices on single-look complex (SLC) format resolution. The algorithm is inspired by recent work with guide…
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four a…
ClassificationGeneral ClassificationKernel based low-rank sparse model for single image super-resolution
Self-similarity learning has been recognized as a promising method for single image super-resolution (SR) to produce high-resolution (HR) image in recent years. The performance of learning based SR reconstruction, howeve…
Image Super-ResolutionSuper-ResolutionTotal variation reconstruction for compressive sensing using nonlocal Lagrangian multiplier
Total variation has proved its effectiveness in solving inverse problems for compressive sensing. Besides, the nonlocal means filter used as regularization preserves texture better for recovered images, but it is quite c…
Compressive SensingWeighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model
Image interpolation is a special case of image super-resolution, where the low-resolution image is directly down-sampled from its high-resolution counterpart without blurring and noise. Therefore, assumptions adopted in …
ClusteringImage Super-ResolutionregressionSuper-Resolution+1