TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
Learning a deep convolutional network for image super-resolution
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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.
The NTIRE 2026 mobile real-world image super-resolution challenge received 16 valid submissions and overviews methods balancing image quality with mobile execution speed.
The NTIRE 2026 ×4 super-resolution challenge benchmarks 31 teams on bicubic-downsampled images using PSNR for the restoration track and perceptual scores for the realism track.
citing papers explorer
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Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework
TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
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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.
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The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
The NTIRE 2026 mobile real-world image super-resolution challenge received 16 valid submissions and overviews methods balancing image quality with mobile execution speed.
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The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview
The NTIRE 2026 ×4 super-resolution challenge benchmarks 31 teams on bicubic-downsampled images using PSNR for the restoration track and perceptual scores for the realism track.