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 2526–2550 of 3874 papers

TitleStatusHype
SRWarp: Generalized Image Super-Resolution under Arbitrary TransformationCode1
Temporal Modulation Network for Controllable Space-Time Video Super-ResolutionCode1
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging—0
A Two-Stage Attentive Network for Single Image Super-ResolutionCode1
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy—0
Attention in Attention Network for Image Super-ResolutionCode1
Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS—0
Kernel Adversarial Learning for Real-world Image Super-resolution—0
VSpSR: Explorable Super-Resolution via Variational Sparse Representation—0
Multitask Learning for VVC Quality Enhancement and Super-Resolution—0
BAM: A Balanced Attention Mechanism for Single Image Super ResolutionCode1
Image Super-Resolution via Iterative RefinementCode1
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-ResolutionCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging—0
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and BaselineCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Context-self contrastive pretraining for crop type semantic segmentationCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
NU-Wave: A Diffusion Probabilistic Model for Neural Audio UpsamplingCode1
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified