Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows. (arXiv:2210.07751v1 [eess.IV])

Remote sensing images (RSIs) in real scenes may be disturbed by multiple
factors such as optical blur, undersampling, and additional noise, resulting in
complex and diverse degradation models. At present, the mainstream SR
algorithms only consider a single and fixed degradation (such as bicubic
interpolation) and cannot flexibly handle complex degradations in real scenes.
Therefore, designing a super-resolution (SR) model that can cope with various
degradations is gradually attracting the attention of researchers. Some studies
first estimate the degradation kernels …

arxiv images remote sensing stochastic