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 261–270 of 3874 papers

TitleStatusHype
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-ResolutionCode1
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large InputCode1
Adaptive Cross-Layer Attention for Image RestorationCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
Burstormer: Burst Image Restoration and Enhancement TransformerCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified