FlowSR enables single-step image super-resolution by learning a rectified flow from LR to HR with consistency distillation, HR regularization, and dual fast-slow timestep scheduling.
Distillation-free one-step diffusion for real-world image super-resolution
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3representative citing papers
DVFace uses a spatio-temporal dual-codebook and asymmetric fusion in a one-step diffusion model to deliver better video face restoration quality, temporal consistency, and identity preservation than recent methods.
The NTIRE 2026 challenge establishes a benchmark for x4 super-resolution of remote sensing infrared images, with 13 teams submitting valid methods evaluated on a dedicated dataset.
citing papers explorer
-
Fast Image Super-Resolution via Consistency Rectified Flow
FlowSR enables single-step image super-resolution by learning a rectified flow from LR to HR with consistency distillation, HR regularization, and dual fast-slow timestep scheduling.
-
DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration
DVFace uses a spatio-temporal dual-codebook and asymmetric fusion in a one-step diffusion model to deliver better video face restoration quality, temporal consistency, and identity preservation than recent methods.
-
The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
The NTIRE 2026 challenge establishes a benchmark for x4 super-resolution of remote sensing infrared images, with 13 teams submitting valid methods evaluated on a dedicated dataset.