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 301–310 of 3874 papers

TitleStatusHype
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion PriorCode1
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse ArraysCode1
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote SensingCode1
Simultaneous Tri-Modal Medical Image Fusion and Super-Resolution using Conditional Diffusion ModelCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
NTIRE 2024 Challenge on Image Super-Resolution (4): Methods and ResultsCode1
LIPT: Latency-aware Image Processing TransformerCode1
Collaborative Feedback Discriminative Propagation for Video Super-ResolutionCode1
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified