AI Image Processing Pipeline for Adaptive ISP Quality
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Solution Overview
Problem
Conventional Image Signal Processors (ISPs) face limitations in image quality and complexity, requiring manual tuning of numerous parameters, and struggle to adapt to varying imaging conditions, while traditional signal processing methods are insufficient for achieving high-quality results.
Innovation Solution
An image processor utilizing a series of trained artificial intelligence models, including denoising, demosaicing, and dynamic range compression, implemented using deep learning to enhance image quality and adapt to varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional signal processing methods are used in ISP pipeline, then processing speed is fast, but image quality is limited
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with artificial intelligence-based processing. The ISP pipeline uses trained AI models (neural networks) to perform demosaicing, denoising, and other image processing tasks, substituting conventional algorithmic approaches with learning-based systems that adapt to image content and achieve superior quality results.
2Ease of manufacture
If conventional ISP methods are used, then implementation is straightforward, but numerous parameters require manual tuning
Solution Approach 1:
The patent implements self-service through automated parameter optimization using AI techniques. The system automatically determines optimal processing parameters by learning from training data, eliminating the need for manual tuning of numerous ISP parameters. The trained models adapt to different sensors and imaging conditions automatically, reducing implementation complexity despite the underlying sophisticated processing.
3Adaptability or versatility
If traditional ISP pipeline is used, then processing is efficient, but adaptability to varying imaging conditions is poor
Solution Approach 1:
The patent applies dynamics by making the ISP pipeline adaptive and flexible. The AI-based processing stages can dynamically adjust their behavior based on input image characteristics and imaging conditions. The system processes images through multiple specialized stages (demosaicing, denoising, etc.) that adapt their parameters and processing strategies according to the specific scene and sensor characteristics, enabling versatility across varying conditions.
Data Source
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AI summary
An image processor comprising a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.