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 1651–1675 of 3874 papers

TitleStatusHype
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution—0
Anatomically Guided Motion Correction for Placental IVIM Parameter Estimation with Accelerated Sampling Method—0
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime—0
Diffusion Image Prior—0
Diffusion-Based Signed Distance Fields for 3D Shape Generation—0
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data—0
Diffusion-based Light Field Synthesis—0
Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios—0
DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images—0
Boomerang: Local sampling on image manifolds using diffusion models—0
DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration—0
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
AdderSR: Towards Energy Efficient Image Super-Resolution—0
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models—0
BOLD: Boolean Logic Deep Learning—0
DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits—0
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics—0
An approach to image denoising using manifold approximation without clean images—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified