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 781–790 of 3874 papers

TitleStatusHype
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
Super-Resolving Blurry Images with Events—0
Machine learning for reconstruction of polarity inversion lines from solar filamentsCode0
Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas—0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
Frequency-Assisted Mamba for Remote Sensing Image Super-ResolutionCode2
Teacher-Student Network for Real-World Face Super-Resolution with Progressive Embedding of Edge Information—0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
All-in-One Deep Learning Framework for MR Image Reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified