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 2681–2690 of 3874 papers

TitleStatusHype
SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation—0
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution—0
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs—0
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis—0
SuperGaussian: Repurposing Video Models for 3D Super Resolution—0
SuperGS: Consistent and Detailed 3D Super-Resolution Scene Reconstruction via Gaussian Splatting—0
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting—0
SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution—0
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis—0
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified