Rearranger Circuit for Neural Network Color Filter Array Processing

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Solution Overview

Problem

Existing image recognition systems face inefficiencies in processing raw image data from color filter arrays, requiring extensive preprocessing like demosaicing and noise reduction, which increases computation and memory requirements.

Innovation Solution

A rearranger circuit is used to process raw image data into raw color channel arrays, bypassing the need for full color conversion, thereby reducing processing time and memory requirements by directly inputting data according to a color filter array pattern.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If full color image preprocessing (demosaicing, noise reduction, color conversion) is performed before neural network processing, then image quality is improved, but computation time and memory requirements increase

Engineering Contradiction:

Improveimage qualityVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts and eliminates the intermediate preprocessing steps (demosaicing, noise reduction, color conversion) from the traditional image processing pipeline. Instead of converting raw CFA data to full color images first, the system directly feeds the rearranged raw color channel arrays into the neural network, taking out the unnecessary intermediate processing stages while maintaining image quality through direct neural network processing of the raw data

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent inverts the traditional processing sequence by rearranging the raw CFA data into separate color channel arrays before neural network input, rather than converting to full color images first. This inversion of the processing order allows the neural network to handle the raw data directly, eliminating the need for preliminary demosaicing and color conversion operations

Inventive Principle:

Principle #13The other way round (Inversion)

2Measurement precision

If full color image preprocessing (demosaicing, noise reduction, color conversion) is performed before neural network processing, then image quality is improved, but device complexity increases

Engineering Contradiction:

Improveimage qualityVSAvoidprocessing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent removes the complex intermediate preprocessing modules (demosaicing algorithms, noise reduction filters, color conversion matrices) from the processing pipeline. By extracting these unnecessary intermediate steps and feeding raw rearranged data directly to the neural network, the system reduces device complexity while maintaining image quality through the neural network's inherent processing capabilities

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The neural network is designed to handle multiple functions simultaneously: it processes the raw color channel data, performs feature extraction, and generates classification results all in one unified processing stage. This multi-functionality eliminates the need for separate preprocessing modules, reducing overall device complexity while maintaining comprehensive image processing capability

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If full color image preprocessing (demosaicing, noise reduction, color conversion) is performed before neural network processing, then image quality is improved, but memory requirements increase

Engineering Contradiction:

Improveimage qualityVSAvoidmemory requirements

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts and eliminates the intermediate full color image representations from the processing pipeline. By removing the demosaicing and color conversion steps that would generate large intermediate full color image data, the system significantly reduces memory requirements while still maintaining image quality through direct neural network processing of the compact raw color channel arrays

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If raw image data is processed directly without full color conversion, then processing efficiency is improved, but neural network training accuracy may worsen

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtraining accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent changes the data representation parameters by rearranging the raw CFA data into separate color channel arrays (R, G1, G2, B) as input to the neural network, rather than converting to standard RGB full color images. This parameter change in data organization maintains processing efficiency while providing the neural network with structured raw data that preserves all original color information for accurate training

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12198294B2Neural network processing of reordered color filter array formatted image data

Publication Date: 2025.01.14 XILINX INC

AI summary

A rearranger circuit rearranges data elements of each raw image of a plurality of raw images according to a plurality of raw color channel arrays. The data elements of each raw image are input to the rearranger circuit according to instances of a pattern of color channels of a color filter array (CFA). The data elements specify values of the color channels in the instances of the pattern, and each raw color channel array has the data elements of one color channel of the color channels in the instances of the pattern. The rearranger circuit can be used in neural network training or in generating raw color channel arrays for performing neural network inference.