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 2876–2900 of 3874 papers

TitleStatusHype
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Context Reasoning Attention Network for Image Super-Resolution—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
SIGNET: Efficient Neural Representation for Light Fields—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
Frequency Consistent Adaptation for Real World Super Resolution—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Attention-based Image Upsampling—0
Projected Distribution Loss for Image EnhancementCode0
Polyblur: Removing mild blur by polynomial reblurring—0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
CT Super Resolution via Zero Shot Learning—0
Learning-Based Quality Assessment for Image Super-Resolution—0
Geometry Enhancements from Visual Content: Going Beyond Ground Truth—0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution—0
Detailed 3D Human Body Reconstruction from Multi-view Images Combining Voxel Super-Resolution and Learned Implicit Representation—0
Super-resolution Guided Pore Detection for Fingerprint Recognition—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified