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 1731–1740 of 3874 papers

TitleStatusHype
DEM Super-Resolution with EfficientNetV2—0
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors—0
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—0
Accelerated WGAN update strategy with loss change rate balancing—0
Delta-WKV: A Novel Meta-in-Context Learner for MRI Super-Resolution—0
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image—0
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds—0
Del-Net: A Single-Stage Network for Mobile Camera ISP—0
Degrees of freedom for off-the-grid sparse estimation—0
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified