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 421–430 of 3874 papers

TitleStatusHype
Perceptually Optimized Super Resolution—0
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
ΩSFormer: Dual-Modal Ω-like Super-Resolution Transformer Network for Cross-scale and High-accuracy Terraced Field Vectorization Extraction—0
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-ResolutionCode2
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
ZoomLDM: Latent Diffusion Model for multi-scale image generationCode1
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution—0
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training—0
FFT-Enhanced Low-Complexity Near-Field Super-Resolution Sensing—0
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified