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 1901–1910 of 3874 papers

TitleStatusHype
Image Super-Resolution with Deep DictionaryCode1
Boosting Video Super Resolution with Patch-Based Temporal Redundancy OptimizationCode1
Rethinking Alignment in Video Super-Resolution TransformersCode1
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution—0
Enhancing Space-time Video Super-resolution via Spatial-temporal Feature InteractionCode1
Towards Lightweight Super-Resolution with Dual Regression LearningCode2
Stochastic Attribute Modeling for Face Super-Resolution—0
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical FidelityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified