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 941–950 of 3874 papers

TitleStatusHype
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Learning Local Implicit Fourier Representation for Image WarpingCode1
Learning Non-linear Wavelet Transformation via Normalizing FlowCode1
Exploring Sparsity in Image Super-Resolution for Efficient InferenceCode1
B-Spline Texture Coefficients Estimator for Screen Content Image Super-ResolutionCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby ReferencesCode1
Structure Aggregation for Cross-Spectral Stereo Image Guided DenoisingCode1
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified