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Deep Neural Networks for Image-Based Dietary Assessment
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Tchebichef Transform Domain-Based Deep Learning Architecture for Image Super-Resolution.

Ahlad Kumar, Harsh Vardhan Singh, Vijeta Khare

    IEEE Transactions on Neural Networks and Learning Systems
    |July 13, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel deep learning approach for image super-resolution (SR) using the Tchebichef transform domain. The TTDSR architecture enhances image quality, including medical images, with fewer parameters.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Deep learning has significantly advanced image super-resolution (SR).
    • Existing methods learn nonlinear mappings from low-resolution (LR) to high-resolution (HR) images.
    • SR is crucial for various research and application areas.

    Purpose of the Study:

    • To propose a deep learning-based image SR architecture in the Tchebichef transform domain.
    • To leverage the Tchebichef transform for simplified SR by utilizing image frequency representations.
    • To evaluate the architecture's performance on medical images, specifically COVID-19 X-ray and CT scans.

    Main Methods:

    • Developed a deep learning architecture integrating a Tchebichef convolutional layer (TCL) for spatial-to-transform domain conversion.
    • Implemented an inverse TCL (ITCL) layer for transform-to-spatial domain conversion.
    • Employed a transfer learning approach for enhancing medical image quality.

    Main Results:

    • The Tchebichef transform domain simplifies the SR task by exploiting image frequency characteristics.
    • The proposed TTDSR architecture successfully enhances the quality of COVID-19 X-ray and CT images.
    • Experimental results show competitive performance compared to existing deep learning methods with fewer trainable parameters.

    Conclusions:

    • The TTDSR architecture offers an effective deep learning-based solution for image super-resolution.
    • The Tchebichef transform domain integration proves beneficial for SR tasks.
    • The method shows promise for improving medical image quality for clinical diagnosis.