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 271–280 of 3874 papers

TitleStatusHype
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks—0
Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation—0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Fast Omni-Directional Image Super-Resolution: Adapting the Implicit Image Function with Pixel and Semantic-Wise Spherical Geometric PriorsCode0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits—0
Bridging high resolution sub-cellular imaging with physiologically relevant engineered tissues—0
DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified