BLUR KERNEL’S EFFECT ON PERFORMANCE OF SINGLE-FRAME SUPER-RESOLUTION ALGORITHMS FOR SPATIALLY ENHANCING HYPERION AND PRISMA DATA
Single-frame super-resolution (SFSR) achieves the goal of generating a high-resolution image from a single low-resolution input in a three-step process, namely, noise removal, up-sampling and deblurring. Scale factor and blur kernel are essential parameters of the up-sampling and deblurring steps. F...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-07-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-M-2-2022/165/2022/isprs-archives-XLVI-M-2-2022-165-2022.pdf |
| Summary: | Single-frame super-resolution (SFSR) achieves the goal of generating a high-resolution image from a single low-resolution input in a three-step process, namely, noise removal, up-sampling and deblurring. Scale factor and blur kernel are essential parameters of the up-sampling and deblurring steps. Few studies document the impact of these parameters on the performance of SFSR algorithms for improving the spatial resolution of real-world remotely-sensed datasets. Here, the effect of changing blur kernel has been studied on the behaviour of two classic SFSR algorithms: iterative back projection (IBP) and gaussian process regression (GPR), which are applied to two spaceborne hyperspectral datasets for scale factors 2, 3 and 4. Eight full-reference image quality metrics and algorithm processing time are deployed for this purpose. A literature-based re-interpretation of Wald’s reduced resolution protocol has also been used in this work for choosing the reference image. Intensive intra-algorithm comparisons of various simulation scenarios reveal each algorithm’s best performing Gaussian blur kernel parameters. Inter-algorithm comparison shows the better performing algorithm out of the two, thereby paving the way for further research in SFSR of remotely-sensed images. |
|---|---|
| ISSN: | 1682-1750 2194-9034 |