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 1926–1950 of 3874 papers

TitleStatusHype
Memory Efficient Patch-based Training for INR-based GANs—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution—0
Variational Deep Image RestorationCode1
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
On the modern deep learning approaches for precipitation downscaling—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans—0
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution—0
A Fast Text-Driven Approach for Generating Artistic Content—0
Localisation And Imaging Methods for Moving Target Ghost Imaging Radar Based On Correlation Intensity Weighting—0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness—0
NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling RatesCode2
Real-World Single Image Super-Resolution Under Rainy Condition—0
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds—0
Super-resolution image display using diffractive decoders—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
Structured Sparsity Learning for Efficient Video Super-ResolutionCode1
AnimeSR: Learning Real-World Super-Resolution Models for Animation VideosCode2
Hypernetwork-Based Adaptive Image RestorationCode1
Real-World Light Field Image Super-Resolution via Degradation ModulationCode1
RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT ScansCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified