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 951–975 of 3874 papers

TitleStatusHype
Diffusion Models Beat GANs on Image ClassificationCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and LatencyCode1
Light Field Image Super-Resolution Using Deformable ConvolutionCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
Light Field Image Super-Resolution with TransformersCode1
Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency RegularizationCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
Lightweight image super-resolution with enhanced CNNCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
Lightweight Modules for Efficient Deep Learning based Image RestorationCode1
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature LearningCode1
Diffusion Prior Interpolation for Flexibility Real-World Face Super-ResolutionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
Bridging Component Learning with Degradation Modelling for Blind Image Super-ResolutionCode1
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: ReportCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
LKFormer: Large Kernel Transformer for Infrared Image Super-ResolutionCode1
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-ResolutionCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified