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 101–110 of 3874 papers

TitleStatusHype
Revealing economic facts: LLMs know more than they say—0
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation—0
Semantic-Guided Diffusion Model for Single-Step Image Super-ResolutionCode1
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-ResolutionCode0
PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient SimulationsCode0
Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and BenchmarkCode0
Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified