3 accepted papers
Single image super-resolution (SISR) often struggles to reconstruct high-resolution (HR) details from heavily degraded low-resolution (LR) inputs. Instead, reference-based super-resolution (RefSR) methods offer an alternative solution to generate promising results using high-quality reference (Ref)…
Capturing the spatial patterns of neurons and generating high-fidelity morphological data remain critical challenges in developing biologically realistic large-scale brain network models. Existing methods fail to reconcile anatomical complexity with diversity and computational scalability. We propos…
Although existing image deep learning super-resolution (SR) methods achieve promising performance on benchmark datasets, they still suffer from severe performance drops when the degradation of the low-resolution (LR) input is not covered in training. To address the problem, we propose an innovative…