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 2881–2890 of 3874 papers

TitleStatusHype
SIGNET: Efficient Neural Representation for Light Fields—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Super Resolve Dynamic Scene From Continuous Spike Streams—0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
Frequency Consistent Adaptation for Real World Super Resolution—0
Deep Learning Techniques for Super-Resolution in Video Games—0
Attention-based Image Upsampling—0
Projected Distribution Loss for Image EnhancementCode0
Polyblur: Removing mild blur by polynomial reblurring—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified