18591265. IMAGE PROCESSING METHOD, IMAGE PROCESSING APPARATUS, METHOD FOR MAKING LEARNED MODEL, LEARNING APPARATUS, IMAGE PROCESSING SYSTEM, AND STORAGE MEDIUM simplified abstract (CANON KABUSHIKI KAISHA)
IMAGE PROCESSING METHOD, IMAGE PROCESSING APPARATUS, METHOD FOR MAKING LEARNED MODEL, LEARNING APPARATUS, IMAGE PROCESSING SYSTEM, AND STORAGE MEDIUM
Organization Name
Inventor(s)
YOSHINORI Kimura of Tochigi (JP)
IMAGE PROCESSING METHOD, IMAGE PROCESSING APPARATUS, METHOD FOR MAKING LEARNED MODEL, LEARNING APPARATUS, IMAGE PROCESSING SYSTEM, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18591265 titled 'IMAGE PROCESSING METHOD, IMAGE PROCESSING APPARATUS, METHOD FOR MAKING LEARNED MODEL, LEARNING APPARATUS, IMAGE PROCESSING SYSTEM, AND STORAGE MEDIUM
The abstract of this patent application describes an image processing method that involves acquiring image information and generating a second image using a quantized machine learning model based on either the acquired image information or predetermined second image information.
- Simplified Explanation:
This patent application discusses a method for enhancing images using machine learning models based on acquired or predetermined image information.
- Key Features and Innovation:
- Acquiring image information about imaging or development conditions - Generating a second image using a quantized machine learning model - Choosing between acquired or predetermined image information for enhancement based on a threshold value
- Potential Applications:
- Image enhancement in various industries such as healthcare, surveillance, and photography - Improving image quality in low-light or high-noise environments
- Problems Solved:
- Enhancing image quality based on specific conditions - Automating image enhancement processes using machine learning models
- Benefits:
- Improved image quality and clarity - Efficient and automated image enhancement processes - Enhanced performance in various imaging applications
- Commercial Applications:
Title: Automated Image Enhancement Technology for Various Industries This technology can be used in medical imaging, security surveillance, and digital photography industries to enhance image quality and improve overall performance. It can streamline image processing workflows and provide better results in challenging imaging conditions.
- Prior Art:
Readers can explore prior art related to image processing methods, machine learning models, and image enhancement technologies to understand the evolution of this field and potential areas for improvement.
- Frequently Updated Research:
Researchers are continuously exploring new techniques and algorithms for image enhancement using machine learning models. Stay updated on the latest advancements in this field to leverage cutting-edge technologies for image processing applications.
Questions about Image Processing Technology: 1. How does this image processing method compare to traditional image enhancement techniques? This method offers automated image enhancement based on specific conditions, providing more efficient and consistent results compared to manual editing processes.
2. What are the potential limitations of using machine learning models for image enhancement? Machine learning models may require extensive training data and computational resources, and their performance can vary based on the quality of input images and model parameters.
Original Abstract Submitted
An image processing method includes a first step of acquiring a first image and first image information about an imaging condition or a development condition corresponding to the first image, and a second step of generating a second image by enhacing the first image using a quantized machine learning model. In the second step, either the first image information or predetermined second image information is used as information to generate the second image, and a determination of whether to use either the first image information or the predetermined second image information as the information to generate the second image is based on a value relating to the first image information and a first threshold.
