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 2371–2380 of 3874 papers

TitleStatusHype
Finding Discriminative Filters for Specific Degradations in Blind Super-ResolutionCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Fourier Series Expansion Based Filter Parametrization for Equivariant ConvolutionsCode1
Improving Multi-View Stereo via Super-Resolution—0
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN—0
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-ResolutionCode1
LAConv: Local Adaptive Convolution for Image Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified