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 1891–1900 of 3874 papers

TitleStatusHype
Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural NetworkCode0
Towards Interpretable Video Super-Resolution via Alternating OptimizationCode1
Deep Audio Waveform PriorCode1
CADyQ: Content-Aware Dynamic Quantization for Image Super-ResolutionCode1
Semantic uncertainty intervals for disentangled latent spacesCode0
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic ImagingCode0
Efficient Meta-Tuning for Content-aware Neural Video DeliveryCode0
HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images—0
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Image Super-Resolution with Deep DictionaryCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified