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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 3874 papers

TitleStatusHype
Super-resolution of biomedical volumes with 2D supervision—0
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencesCode2
Differentiable Search for Finding Optimal Quantization Strategy—0
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral ImagesCode0
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
LIPT: Latency-aware Image Processing TransformerCode1
Space-Time Video Super-resolution with Neural Operator—0
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data—0
Rethinking Diffusion Model for Multi-Contrast MRI Super-ResolutionCode2
CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
Gull: A Generative Multifunctional Audio Codec—0
Collaborative Feedback Discriminative Propagation for Video Super-ResolutionCode1
Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint—0
PointSAGE: Mesh-independent superresolution approach to fluid flow predictions—0
Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Distortion-aware super-resolution for planetary exploration imagesCode0
Translation-based Video-to-Video Synthesis—0
GenN2N: Generative NeRF2NeRF TranslationCode2
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion DistillationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified