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 276–300 of 3874 papers

TitleStatusHype
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data CharacteristicCode1
Detail-Preserving Transformer for Light Field Image Super-ResolutionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
AdaDM: Enabling Normalization for Image Super-ResolutionCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Designing a Practical Degradation Model for Deep Blind Image Super-ResolutionCode1
A Benchmark for Chinese-English Scene Text Image Super-resolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion ModelsCode1
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
DynaVSR: Dynamic Adaptive Blind Video Super-ResolutionCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Activating Wider Areas in Image Super-ResolutionCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
A heterogeneous group CNN for image super-resolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified