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 341–350 of 3874 papers

TitleStatusHype
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
Closed-loop Matters: Dual Regression Networks for Single Image Super-ResolutionCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Combining Attention Module and Pixel Shuffle for License Plate Super-ResolutionCode1
Discrete Cosine Transform Network for Guided Depth Map Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
DL4DS -- Deep Learning for empirical DownScalingCode1
DSR: Towards Drone Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified