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 851–860 of 3874 papers

TitleStatusHype
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Super-Resolution Analysis for Landfill Waste Classification—0
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
Video Interpolation with Diffusion Models—0
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified