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 661–670 of 3874 papers

TitleStatusHype
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal FusionCode1
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projectionCode1
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution NetworksCode1
Consistent Direct Time-of-Flight Video Depth Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
KXNet: A Model-Driven Deep Neural Network for Blind Super-ResolutionCode1
Does Diffusion Beat GAN in Image Super Resolution?Code1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified