Raw Bayer CNN Analytics Without ISP Pipeline Latency
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Solution Overview
Problem
Existing image processing systems face inefficiencies in edge computing due to the need for image signal processing (ISP) pipelines, which introduce artifacts and increase latency, and lack of labeled raw Bayer image datasets for training convolutional neural networks (CNNs) for direct image analytics.
Innovation Solution
A generative model is used to convert RGB images to raw Bayer format, training a CNN to directly process sensor data without ISP steps, using unlabeled raw Bayer images to synthesize labeled datasets, and employing a single CNN for direct image analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ISP pipeline is used to process raw Bayer image data, then image quality is improved, but computation cost and latency increase
Solution Approach 1:
The patent extracts and removes the traditional ISP pipeline from the image processing system. Instead of using multiple ISP steps (demosaicing, white balance, color correction, sharpening, noise reduction) followed by CNN processing, the invention directly feeds raw Bayer data into a single CNN model, eliminating the intermediate ISP processing steps that cause latency while maintaining image quality through the CNN's direct learning approach
Solution Approach 2:
The patent merges the ISP functions and CNN processing into a single integrated CNN model. The CNN is trained to perform both the traditional ISP tasks and the image analytics classification in one unified processing step, combining multiple functions into a single computational pipeline that reduces latency and computation cost
2Reliability
If traditional ISP pipeline is used to process raw Bayer image data, then image quality is improved, but computation cost increases
Solution Approach 1:
The patent removes the computationally expensive traditional ISP pipeline (including demosaicing, white balance, color correction, sharpening, and noise reduction steps) and replaces it with a direct CNN processing approach that operates on raw Bayer data, significantly reducing computation cost and energy consumption while maintaining image quality
Solution Approach 2:
Instead of following the conventional approach of first processing the image through multiple ISP steps to improve quality and then performing classification, the patent inverts the workflow by directly classifying raw Bayer data using CNN, achieving both quality and efficiency through this reversed processing order
3Ease of manufacture
If CNN is trained using RGB images, then labeled dataset is available, but direct processing of raw Bayer data is not achieved
Solution Approach 1:
The patent introduces an intermediary synthesis process that generates synthetic raw Bayer images from available RGB images and their corresponding labels. This intermediary step creates a synthetic labeled Bayer dataset that enables the CNN to be trained for direct raw Bayer processing, bridging the gap between available RGB data and the need for Bayer-specific training data
Solution Approach 2:
The patent performs preliminary data synthesis before CNN training by generating synthetic labeled Bayer datasets from RGB images. This preliminary action creates the necessary training data structure that enables subsequent direct Bayer processing capability without requiring actual labeled raw Bayer images to be manually collected
Data Source
AI summary
Systems and methods for performing direct conversion of image sensor data to image analytics are provided. One such system for directly processing sensor image data includes a sensor configured to capture an image and generate corresponding image data in a raw Bayer format, and a convolution neural network (CNN) coupled to the sensor and configured to generate image analytics directly from the image data in the raw Bayer format. Systems and methods for training the CNN are provided, and may include a generative model that is configured to convert RGB images into estimated images in the raw Bayer format.