Image Sensor Remosaicing With Adaptive Noise and Sharpness Inputs
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Solution Overview
Problem
Existing remosaicing technologies, particularly deep learning-based methods, struggle to adapt to various conditions due to training under fixed conditions, leading to challenges in generating high-quality images from non-Bayer patterns.
Innovation Solution
An image sensor and processing apparatus that includes a pixel array, readout circuit, and processor, which utilizes a machine learning model to generate RGB images by inputting noise reduction and sharpness information, enabling the conversion of non-Bayer image data into high-quality Bayer pattern images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based remosaicing is used to generate high-quality images, then image quality is improved, but adaptability to various conditions deteriorates because models are trained under fixed conditions
Solution Approach 1:
The patent makes the remosaicing model dynamic by enabling it to adapt to different shooting conditions (e.g., indoor, outdoor, night mode) through condition-specific training data and adjustable model parameters. This allows the system to switch between different remosaicing strategies based on the input image characteristics, resolving the contradiction between maintaining high image quality and adapting to various conditions.
Solution Approach 2:
The patent changes key parameters of the remosaicing process by training models with different condition parameters (lighting conditions, scene types) and allowing selective application based on detected image characteristics. This parameter-based adaptation enables the system to maintain high manufacturing precision across diverse conditions rather than using a single fixed model.
2Productivity
If rule-based remosaicing is used, then processing speed is improved, but image quality deteriorates compared to deep learning-based methods
Solution Approach 1:
The patent segments the remosaicing process into multiple stages: initial fast rule-based remosaicing followed by selective deep learning refinement. This segmentation allows the system to achieve both high processing speed for routine cases and high image quality for challenging cases, resolving the contradiction between productivity and manufacturing precision.
Solution Approach 2:
The patent introduces an intermediary assessment mechanism that evaluates input images and determines whether they require full deep learning processing or can be handled by faster rule-based methods. This intermediary step enables intelligent routing that balances processing speed and image quality based on actual needs.
3Device complexity
If a single remosaicing model is used for all conditions, then device complexity is reduced, but image quality deteriorates under specific conditions
Solution Approach 1:
The patent creates a universal remosaicing system that can handle multiple shooting conditions through a multi-functional architecture. Instead of separate dedicated models for each condition, the system uses a unified framework that selects and applies appropriate processing strategies based on input characteristics, reducing overall device complexity while maintaining high image quality across conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively generates high-quality RGB images by incorporating noise reduction and sharpness improvements, enhancing image quality through adaptive remosaicing.
Implementation Method 1
a pixel array configured to correspond to a first color pattern and to convert a received optical signal into an electrical signal
Data Source
AI summary
Provided is an apparatus for processing images, the apparatus including: a pixel array configured to correspond to a first color pattern and to convert a received optical signal into an electrical signal; a readout circuit configured to convert the electrical signal into first image data associated with the first color pattern and to output the first image data; a memory storing instructions; and a processor configured to execute the instructions, wherein the one or more instructions, when executed by the at least one processor, cause the apparatus to: receive the first image data, acquire at least one of noise reduction information or sharpness information associated with the first image data, input the first image data and at least one of the noise reduction information or the sharpness information into a machine learning model to generate a first RGB image, and output the first RGB image.