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 1051–1060 of 3874 papers

TitleStatusHype
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale ApproachCode1
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive ConvolutionCode1
Fast Nearest Convolution for Real-Time Efficient Image Super-ResolutionCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
Parallax Attention for Unsupervised Stereo Correspondence LearningCode1
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks—0
Directional diffusion models for graph representation learning—0
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration—0
Anchored Regression Networks Applied to Age Estimation and Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified