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 2651–2675 of 3874 papers

TitleStatusHype
Transformers in Vision: A Survey—0
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningCode1
Context Reasoning Attention Network for Image Super-Resolution—0
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning SchemeCode1
SIGNET: Efficient Neural Representation for Light Fields—0
Event Stream Super-Resolution via Spatiotemporal Constraint Learning—0
Benchmarking Ultra-High-Definition Image Super-Resolution—0
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and Super-Resolution—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective—0
Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGANCode1
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrastCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution NetworksCode1
Frequency Consistent Adaptation for Real World Super Resolution—0
Attention-based Image Upsampling—0
Zoom-to-Inpaint: Image Inpainting with High-Frequency DetailsCode1
Deep Learning Techniques for Super-Resolution in Video Games—0
Neural Radiance Flow for 4D View Synthesis and Video ProcessingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified