SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 231–240 of 3874 papers

TitleStatusHype
QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution—0
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution—0
From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-ResolutionCode3
Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling—0
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
GenDR: Lightning Generative Detail Restorator—0
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-ResolutionCode1
Global graph features unveiled by unsupervised geometric deep learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified