RGB-IR Image Sensor Format Conversion Using Precomputed Lookup Tables
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Solution Overview
Problem
RGB-IR image sensors require interpolation to convert single-color component pixels into RGB pixels, as they can only sense light intensities and not distinguish colors, leading to inefficiencies in obtaining missing color components for image conversion.
Innovation Solution
A method for image format conversion in RGB-IR image sensors involves selecting a center pixel and its surrounding pixel unit, determining the basic and missing color components, calculating average intensity values, and assigning these values to the center pixel to interpolate the missing color components, thereby converting the image format from RGB-IR to RGB.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If interpolation is used to convert single-color component pixels into RGB pixels, then color information is obtained, but processing time and computational complexity increase
Solution Approach 1:
The patent pre-calculates and stores conversion coefficients and lookup tables during system initialization or manufacturing. These pre-computed values are stored in memory for rapid retrieval during runtime, eliminating the need for complex real-time calculations when converting single-color pixels to RGB values.
Solution Approach 2:
The patent uses lookup tables that contain pre-stored conversion data and coefficient sets. Instead of performing complex interpolation calculations for each pixel, the system copies pre-computed values from these tables based on the specific pixel type and position, dramatically reducing processing time while maintaining conversion accuracy.
2Measurement precision
If complex interpolation algorithms are used to obtain missing color components, then conversion accuracy improves, but device complexity increases
Solution Approach 1:
The patent pre-calculates conversion coefficients and stores them in lookup tables during system initialization. These pre-computed values encapsulate complex interpolation logic, allowing the runtime system to achieve high conversion accuracy through simple table lookups and basic arithmetic operations rather than executing complex algorithms in real-time.
Solution Approach 2:
The patent replaces complex, resource-intensive interpolation algorithms with lightweight lookup tables and simple arithmetic operations. This substitution uses inexpensive, easily implementable methods (table lookups and basic math) to achieve the same functional result, reducing device complexity while maintaining conversion accuracy.
3Manufacturing precision
If real-time conversion is performed for each pixel, then image quality is maintained, but processing speed decreases
Solution Approach 1:
The patent pre-computes conversion coefficients and stores them in lookup tables before runtime. During actual image conversion, the system retrieves these pre-computed values and applies them through simple arithmetic operations, maintaining image quality while dramatically improving processing speed compared to performing full interpolation calculations in real-time.
Solution Approach 2:
The patent uses pre-stored conversion data and coefficient sets from lookup tables. Instead of calculating conversion values for each pixel in real-time, the system copies appropriate pre-computed values from these tables based on pixel characteristics, preserving image quality while enabling faster batch processing of entire images.
Data Source
AI summary
A method for converting an image format applied to an RGB-IR image sensor includes: (Step 1) acquiring an RGB-IR image in digital form; (Step 2) selecting a pixel of the RGB-IR image as a center pixel and selecting a pixel unit with the center pixel at the center; (Step 3) interpolating the center pixel to obtain missing color components of the center pixel; (Step 4) repeating blocks (S2) and (S3) for interpolating each pixel of the RGB-IR image to obtain the missing color component of each pixel; and (Step 5) outputting an image after the interpolation process.