Neural Network Image Signal Processor Using Universality
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Solution Overview
Problem
Current image signal processing techniques require dedicated components and are costly, inflexible, and not well-suited for all environments, as they need to be tailored to specific imaging sensors and do not account for manufacturing variability, making them inefficient and costly to develop and maintain.
Innovation Solution
A neural network, specifically a convolutional neural network, is trained using raw and desired quality images to adjust image quality attributes such as size, brightness, and contrast, which can be downloaded onto multipurpose processors like those found in smartphones, allowing for flexible and efficient image signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If dedicated image signal processing components are used, then image processing quality is improved, but manufacturing cost and device complexity increase
Solution Approach 1:
The patent applies universality by using a general-purpose processor that can perform multiple functions including image signal processing, video processing, and other computational tasks. This eliminates the need for dedicated image signal processing hardware while maintaining processing quality through software-based neural network algorithms.
2Manufacturing precision
If dedicated image signal processing components are used, then image processing quality is improved, but manufacturing cost increases
Solution Approach 1:
The patent uses a universal processor that can handle image processing along with other device functions, eliminating the need for additional dedicated hardware components. This reduces manufacturing costs by leveraging existing hardware resources rather than requiring specialized image processing chips.
Solution Approach 2:
The patent employs software-based neural network models that can be copied and executed on general-purpose processors. This approach replaces expensive dedicated hardware with software implementations that can be distributed and updated without hardware changes, significantly reducing manufacturing costs.
3Measurement precision
If code is tailored to individual sensors, then processing accuracy is improved, but development complexity and time increase
Solution Approach 1:
The patent uses neural network models with adjustable parameters that can be trained and fine-tuned for different sensor types. Instead of rewriting code for each sensor, the same neural network architecture processes images from various sensors by adapting its internal parameters through training, simplifying development while maintaining accuracy.
4Manufacturing precision
If greater image signal processing capability is included, then processing quality is improved, but space required within the device increases
Solution Approach 1:
The patent leverages the existing processor in mobile devices that already occupies necessary space for other computational tasks. By making this existing processor universally capable of image signal processing through software, no additional physical space is required, eliminating the space trade-off.
Data Source
AI summary
An image signal processing (ISP) system is provided. The system includes a neural network trained by inputting a set of raw data images and a correlating set of desired quality output images; the neural network including an input for receiving input image data and providing processed output; wherein the processed output includes input image data that has been adjusted for at least one image quality attribute. A method and an imaging device are disclosed.