TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Methods/PixelShuffle

PixelShuffle

GeneralIntroduced 200045 papers
Source Paper

Description

PixelShuffle is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of 1/r1/r1/r. Specifically it rearranges elements in a tensor of shape (\*,C×r2,H,W)(\*, C \times r^2, H, W)(\*,C×r2,H,W) to a tensor of shape (\*,C,H×r,W×r)(\*, C, H \times r, W \times r)(\*,C,H×r,W×r).

Image Source: Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

Papers Using This Method

Super-Resolution Generative Adversarial Networks based Video Enhancement2025-05-14Uncertainty Estimation for Super-Resolution using ESRGAN2024-12-19OneNet: A Channel-Wise 1D Convolutional U-Net2024-11-14Deep Learning-Based CKM Construction with Image Super-Resolution2024-10-28Cascaded Temporal Updating Network for Efficient Video Super-Resolution2024-08-26Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint2024-04-06Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks2024-01-31Texture and Noise Dual Adaptation for Infrared Image Super-Resolution2023-11-15Guided Frequency Loss for Image Restoration2023-09-27A comparative analysis of SRGAN models2023-07-18Can SAM Boost Video Super-Resolution?2023-05-11Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution2022-10-30A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models2022-10-12Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation2022-09-07Physics-informed Deep Super-resolution for Spatiotemporal Data2022-08-02NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras2022-07-25On the Generalization of BasicVSR++ to Video Deblurring and Denoising2022-04-11Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images2022-04-06High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss2021-07-21Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks2021-05-17