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 221–230 of 3874 papers

TitleStatusHype
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified