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 2401–2410 of 3874 papers

TitleStatusHype
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Rethinking Super-Resolution as Text-Guided Details Generation—0
Perception-Oriented Stereo Image Super-Resolution—0
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification—0
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New BenchmarkCode0
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
Memory Efficient Patch-based Training for INR-based GANs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified