AI Colorization of Filterless Solid-State Imaging Sensors
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Solution Overview
Problem
Conventional image sensors with color filters suffer from light attenuation, leading to reduced visibility and fidelity in low-light conditions, particularly in dark environments where the amount of received light is insufficient, and struggle to accurately capture color images with infrared cameras due to incorrect color representation of materials that absorb infrared rays.
Innovation Solution
An imaging system utilizing a solid-state imaging element without a color filter, combined with a learning device employing AI and neural networks for colorization of monochrome images, which adjusts focus and performs super-resolution processing to enhance image visibility and fidelity in low-light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a color filter is used in the image sensor, then color images can be obtained, but the amount of light reaching the light-receiving region is reduced
Solution Approach 1:
The patent removes the color filter from the optical path of the image sensor, extracting the light-blocking component that causes the contradiction. By taking out the color filter, maximum light reaches the sensor while color information is recovered through AI processing of the captured light intensity data
Solution Approach 2:
The patent replaces the mechanical/optical color filter system with an AI-based colorization system. Instead of using physical filters to separate colors, the system uses machine learning algorithms to infer and generate color information from monochrome light intensity data, substituting a mechanical filtering approach with an intelligent processing approach
2Measurement precision
If a color filter is used in the image sensor, then color information can be captured, but visibility in low-light conditions deteriorates
Solution Approach 1:
The patent extracts and removes the color filter that limits light transmission, allowing maximum light to reach the sensor in low-light conditions. The color information is then recovered through AI processing rather than optical filtering
Solution Approach 2:
The patent introduces AI processing as an intermediary between light capture and color generation. The system captures light intensity data without filters, then uses AI algorithms as a mediator to generate accurate color information, bridging the gap between monochrome capture and color output
3Illumination intensity
If infrared imaging is used in dark environments, then imaging capability is maintained, but color fidelity is lost
Solution Approach 1:
The patent replaces infrared imaging mechanisms with visible light imaging enhanced by AI. Instead of using infrared sensors that cannot capture color, the system uses visible light sensors with AI colorization to achieve both dark environment capability and color fidelity
Solution Approach 2:
The patent changes the operating parameters of the image sensor to maximize light sensitivity in low-light conditions while using AI to maintain color accuracy. The system adjusts to capture maximum light intensity data and uses AI to transform this data into accurate color representations
Data Source
AI summary
Color filters are used for color images obtained using imaging devices such as conventional image sensors. Imaging elements with color filters are sold, and an appropriate combination of the imaging element and a lens or the like is incorporated in an electronic device. Only providing a color filter to overlap a light-receiving region of an image sensor reduces the amount of light reaching the light-receiving region. An imaging system of the present invention includes a solid-state imaging element without a color filter, a storage device, and a learning device. Since the color filter is not included, colorization is performed on obtained monochrome image data (analog data), and coloring is performed using an AI system.