TUDSR applies a twice-upsampling diffusion strategy with chunk-based training to achieve state-of-the-art super-resolution at 1024^2 and 2048^2 resolutions using a one-step GAN on SD2.1-base.
Distillation-free one-step diffusion for real-world image super-resolution
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.
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
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TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
TUDSR applies a twice-upsampling diffusion strategy with chunk-based training to achieve state-of-the-art super-resolution at 1024^2 and 2048^2 resolutions using a one-step GAN on SD2.1-base.
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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.
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Fast Image Super-Resolution via Consistency Rectified Flow
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.
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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.