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 2811–2820 of 3874 papers

TitleStatusHype
Co-learning Single-Step Diffusion Upsampler and Downsampler with Two Discriminators and Distillation—0
Teacher-Student Network for Real-World Face Super-Resolution with Progressive Embedding of Edge Information—0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images—0
Temporal Kernel Consistency for Blind Video Super-Resolution—0
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light—0
Temporal Super-Resolution using Multi-Channel Illumination Source—0
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review—0
Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization—0
Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified