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 1141–1150 of 3874 papers

TitleStatusHype
Super-Resolution Generative Adversarial Networks based Video Enhancement—0
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution—0
Revealing economic facts: LLMs know more than they say—0
DHECA-SuperGaze: Dual Head-Eye Cross-Attention and Super-Resolution for Unconstrained Gaze Estimation—0
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation—0
N^2LoS: Single-Tag mmWave Backscatter for Robust Non-Line-of-Sight Localization—0
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-ResolutionCode0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient SimulationsCode0
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified