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 2851–2860 of 3874 papers

TitleStatusHype
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network—0
Toward Efficient Deep Blind RAW Image Restoration—0
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients—0
Toward Moiré-Free and Detail-Preserving Demosaicking—0
Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models—0
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model—0
Toward Real-World Super-Resolution via Adaptive Downsampling Models—0
Towards a Computer Vision Particle Flow—0
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement—0
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified