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 881–890 of 3874 papers

TitleStatusHype
INCODE: Implicit Neural Conditioning with Prior Knowledge EmbeddingsCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Deep Model-Based Super-Resolution with Non-uniform BlurCode1
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningCode1
Best-Buddy GANs for Highly Detailed Image Super-ResolutionCode1
Learning Non-linear Wavelet Transformation via Normalizing FlowCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
An End-to-end Framework For Low-Resolution Remote Sensing Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified