17493661. SYSTEM AND METHOD OF CONVOLUTIONAL NEURAL NETWORK simplified abstract (TAIWAN SEMICONDUCTOR MANUFACTURING COMPANY, LTD.)

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SYSTEM AND METHOD OF CONVOLUTIONAL NEURAL NETWORK

Organization Name

TAIWAN SEMICONDUCTOR MANUFACTURING COMPANY, LTD.

Inventor(s)

Chao-Tsung Huang of Hsinchu City (TW)

Hsiu-Pin Hsu of New Taipei City (TW)

SYSTEM AND METHOD OF CONVOLUTIONAL NEURAL NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 17493661 titled 'SYSTEM AND METHOD OF CONVOLUTIONAL NEURAL NETWORK

Simplified Explanation

The patent application describes a method and system for generating an output image from an input image using convolutional neural networks (CNN) and non-local operations.

  • The method involves downscaling the input image to create a scaled image.
  • A first CNN modeling process with non-local operations is performed on the scaled image to generate global parameters.
  • A second CNN modeling process with second non-local operations is performed on the input image using the global parameters to generate the output image.

Potential applications of this technology:

  • Image processing and enhancement: The method can be used to improve the quality and resolution of images, making it useful in applications such as image editing, medical imaging, and surveillance systems.
  • Computer vision: The method can be applied to tasks like object recognition, image classification, and scene understanding, improving the accuracy and performance of computer vision systems.

Problems solved by this technology:

  • Limited image quality: The method addresses the problem of low-resolution or poor-quality images by using CNN and non-local operations to enhance the details and overall appearance of the output image.
  • Computational efficiency: By using global parameters and non-local operations, the method reduces the computational complexity compared to traditional CNN approaches, making it more efficient for real-time applications.

Benefits of this technology:

  • Improved image quality: The method enhances the details, sharpness, and overall visual appeal of images, resulting in better image quality for various applications.
  • Faster processing: By utilizing global parameters and non-local operations, the method achieves faster processing times, making it suitable for real-time applications where speed is crucial.
  • Versatility: The method can be applied to a wide range of image processing tasks and computer vision applications, providing a flexible and adaptable solution.


Original Abstract Submitted

A method the following operations: downscaling an input image to generate a scaled image; performing, to the scaled image, a first convolutional neural networks (CNN) modeling process with first non-local operations, to generate global parameters; and performing, to the input image, a second CNN modeling process with second non-local operations that are performed with the global parameters, to generate an output image corresponding to the input image. A system is also disclosed herein.

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