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

Showing 361370 of 3874 papers

TitleStatusHype
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution NetworksCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal LearningCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
ARM: Any-Time Super-Resolution MethodCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
Asymmetric CNN for image super-resolutionCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41Unverified