18276677. CONTRAST BOOST BY MACHINE LEARNING simplified abstract (KONINKLIJKE PHILIPS N.V.)

CONTRAST BOOST BY MACHINE LEARNING

Organization Name

KONINKLIJKE PHILIPS N.V.

Inventor(s)

LIRAN Goshen of PARDES-HANNA (IL)

CONTRAST BOOST BY MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18276677 titled 'CONTRAST BOOST BY MACHINE LEARNING

Simplified Explanation:

The patent application describes a training system for a machine learning model that enhances images. The system includes two generative machine learning models, one of which is part of the target model. Training involves high and low-quality images, with the target model producing an output image based on the input image.

• The system includes two generative machine learning models, one of which is part of the target model.
• Training involves high and low-quality images to enhance image quality.
• The target model produces an output image based on the input image.
• The system includes a training controller to adjust parameters based on the deviation between estimated and actual input images.

Key Features and Innovation:

• Utilizes generative machine learning models for image enhancement.
• Training with high and low-quality images to improve image quality.
• Target model produces output based on input image.
• Training controller adjusts parameters based on deviation between estimated and actual input images.

Potential Applications:

• Image enhancement in photography and video editing.
• Medical imaging for improved diagnostics.
• Satellite imaging for clearer data analysis.

Problems Solved:

• Enhancing image quality without manual intervention.
• Improving accuracy and clarity of images.
• Streamlining the image enhancement process.

Benefits:

• Automated image enhancement.
• Enhanced image quality.
• Improved efficiency in image processing.

Commercial Applications:

The technology can be applied in industries such as photography, healthcare, and satellite imaging for enhanced image quality and improved data analysis.

Questions about Image Enhancement: 1. How does the training system adjust parameters for image enhancement? 2. What are the potential limitations of using generative machine learning models for image enhancement?

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Original Abstract Submitted

A training system for a target machine learning model for image enhancement, and related methods. The system comprises a framework of two machine learning models (G, G) of the generative type, one such model (G) being part of the target machine learning model. The training is based on a training data set including at least two types of training imagery, high image quality, IQ, imagery and low IQ imagery, the training input image (I) being one of the high IQ type. The generative network (G) processes the training input image (I) of the high IQ type to produce a training output image (I) having reduced IQ. The target machine learning model (TM) further produces, based on the training output image (I) and the training input image (I) of the high IQ type, a second training output image (I). The second generator network (G) estimates, from the second training output image (I), an estimate of the training input image of the high IQ type. A training controller (TC) adjusts parameters of the machine learning model framework, based a deviation between the estimate of the training input image of the high IQ type, and the said training input image (I) of the high IQ type.