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 226–250 of 3874 papers

TitleStatusHype
DeFMO: Deblurring and Shape Recovery of Fast Moving ObjectsCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual NetworkCode1
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-AttentionCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
CNN-generated images are surprisingly easy to spot... for nowCode1
Accelerating Diffusion Models for Inverse Problems through Shortcut SamplingCode1
Burst Super-Resolution with Diffusion Models for Improving Perceptual QualityCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Component Divide-and-Conquer for Real-World Image Super-ResolutionCode1
Burst Image Restoration and EnhancementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified