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 2441–2450 of 3874 papers

TitleStatusHype
Incorporating Degradation Estimation in Light Field Spatial Super-Resolution—0
Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution—0
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
Infrared Image Super-Resolution via GAN—0
RGB-Guided Resolution Enhancement of IR Images—0
RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified