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 1831–1840 of 3874 papers

TitleStatusHype
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention—0
Improving Object Detection Quality in Football Through Super-Resolution Techniques—0
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)—0
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis—0
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution—0
Benefiting from Multitask Learning to Improve Single Image Super-Resolution—0
Improving the resolution of microscope by deconvolution after dense scan—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Image Processing GNN: Breaking Rigidity in Super-Resolution—0
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified