Image Sensor Remosaic Processing with Non-Linear Functions
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Solution Overview
Problem
Conventional image processing systems experience information loss and resolution degradation during the remosaic operation, which converts tetra format image data into Bayer format, due to differences in pixel arrangements.
Innovation Solution
An image sensor and processing system that includes a sensing unit, a pre-processing processor applying a non-linear function, an image processor using machine learning to perform remosaic operations, and a post-processing processor applying another non-linear function, thereby generating high-quality output image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a remosaic operation is performed to convert tetra format image data into Bayer format, then the image data can be processed by conventional Bayer format processors, but information loss and resolution degradation occur
Solution Approach 1:
The patent applies preliminary actions by performing multiple preprocessing steps (noise filtering, luminance calculation, edge detection) and postprocessing steps (detail enhancement, artifact reduction) around the remosaic operation. These preparatory and follow-up actions preserve image information that would otherwise be lost during the format conversion process.
Solution Approach 2:
The patent changes processing parameters dynamically by adjusting the remosaic algorithm based on detected image characteristics such as luminance ranges, edge directions, and noise levels. This adaptive parameter adjustment maintains image quality while enabling compatibility with Bayer format processors.
2Adaptability or versatility
If a remosaic operation is performed to convert tetra format image data into Bayer format, then the image data can be processed by conventional Bayer format processors, but resolution degradation occurs
Solution Approach 1:
The patent performs preliminary edge detection and luminance analysis before the remosaic operation to identify critical image features. This preliminary action allows the remosaic process to preserve resolution by directing more processing resources toward maintaining detail in edge regions and high-luminance areas.
Solution Approach 2:
The patent dynamically adjusts remosaic parameters based on local image characteristics, using different interpolation methods for different regions. This parameter adaptation maintains higher resolution in critical areas while still achieving compatibility with Bayer format processors.
3Measurement precision
If multiple processing steps are applied to minimize errors and artifacting, then output image quality increases, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into distinct functional modules: preprocessing (noise filtering, luminance calculation, edge detection), remosaic operation, and postprocessing (detail enhancement, artifact reduction). This segmentation allows each module to be optimized independently while working together to improve overall image quality.
Solution Approach 2:
The patent uses parameter-based control to manage complexity by adjusting the intensity and application of different processing steps based on image characteristics. This allows the system to achieve high quality output while maintaining flexibility in processing complexity based on actual needs.
Data Source
AI summary
According to an embodiment of the present inventive concept, an image sensor includes a sensor including a plurality of pixels and configured to capture incident light and generate raw image data; a pre-processing processor configured to generate first image data by applying a first function to the raw image data; an image processor configured to receive the first image data and generate second image data using a machine learning model; and a post-processing processor configured to generate third image data by applying a second function to the second image data, wherein the first function is a non-linear function.