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 1341–1350 of 3874 papers

TitleStatusHype
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Generalizable One-shot Neural Head Avatar—0
Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks—0
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-ResolutionCode1
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration—0
SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationCode0
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projectionCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified