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
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and ReconstructionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
Deep Blind Video Super-resolutionCode1
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-ResolutionCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified