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 1491–1500 of 3874 papers

TitleStatusHype
Parameter-Free Channel Attention for Image Classification and Super-Resolution—0
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-ResolutionCode1
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-ResolutionCode2
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
LSwinSR: UAV Imagery Super-Resolution based on Linear Swin TransformerCode1
Iterative Soft Shrinkage Learning for Efficient Image Super-ResolutionCode1
Depth Super-Resolution from Explicit and Implicit High-Frequency Features—0
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified