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 251–260 of 3874 papers

TitleStatusHype
ZoomLDM: Latent Diffusion Model for multi-scale image generationCode1
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-ResolutionCode1
Volumetric Conditioning Module to Control Pretrained Diffusion Models for 3D Medical ImagesCode1
Deep Learning-Based CKM Construction with Image Super-ResolutionCode1
Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution NetworkCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
HFH-Font: Few-shot Chinese Font Synthesis with Higher Quality, Faster Speed, and Higher ResolutionCode1
SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-ResolutionCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified