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 1331–1340 of 3874 papers

TitleStatusHype
Learning Likelihoods with Conditional Normalizing FlowsCode0
Conditional Generation Using Polynomial ExpansionsCode0
Light Field Super-resolution via Attention-Guided Fusion of Hybrid LensesCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel RemovalCode0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
Evaluating Robustness of Deep Image Super-Resolution against Adversarial AttacksCode0
Learning Descriptor Networks for 3D Shape Synthesis and AnalysisCode0
L-MAGIC: Language Model Assisted Generation of Images with CoherenceCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified