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 451–475 of 3874 papers

TitleStatusHype
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodlesCode1
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and ResultsCode1
Revisiting Implicit Neural Representations in Low-Level VisionCode1
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B ImageryCode1
Cross-View Hierarchy Network for Stereo Image Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Gated Multi-Resolution Transfer Network for Burst Restoration and EnhancementCode1
Local-Global Temporal Difference Learning for Satellite Video Super-ResolutionCode1
Towards Realistic Ultrasound Fetal Brain Imaging SynthesisCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-ResolutionCode1
Real-time 6K Image Rescaling with Rate-distortion OptimizationCode1
CoReFusion: Contrastive Regularized Fusion for Guided Thermal Super-ResolutionCode1
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
Tunable Convolutions with Parametric Multi-Loss OptimizationCode1
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation TransformerCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super ResolutionCode1
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural RepresentationsCode1
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-ResolutionCode1
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up TablesCode1
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-ResolutionCode1
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-ResolutionCode1
Human Guided Ground-truth Generation for Realistic Image Super-resolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified