Neural Image Processor for Multi-Color-Space ISP Tuning
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Solution Overview
Problem
Traditional Image Signal Processor (ISP) pipelines in digital photography require extensive tuning and are difficult to engineer due to complex dependencies between modules, leading to inconsistent image quality and the need for manual parameter adjustment.
Innovation Solution
A single end-to-end trainable neural network architecture that performs denoising, demosaicing, and color correction using a multi-part artificial intelligence model, allowing independent adjustment of image properties in different color spaces and ensuring consistency through multi-scale contextual fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional ISP pipelines are used with multiple modules, then image processing functions can be performed, but the system becomes complex and requires extensive manual tuning
Solution Approach 1:
The patent combines multiple separate ISP modules (denoising, demosaicing, color correction) into a single integrated neural network model. This unified architecture processes raw sensor data through sequential layers that perform all traditional ISP functions in one cohesive system, eliminating the need for separate module implementation and reducing overall system complexity.
Solution Approach 2:
The neural network model serves multiple image processing functions simultaneously within a single system. The same network performs denoising, demosaicing, and color correction tasks that traditionally required separate dedicated modules, making the system more versatile and easier to implement while maintaining all necessary ISP capabilities.
2Productivity
If traditional ISP modules are used, then specific image processing tasks can be performed, but manual parameter tuning is required for optimal performance
Solution Approach 1:
The neural network model automatically learns and optimizes its own parameters through training on labeled datasets. The model performs self-tuning by adjusting its internal weights and biases during the training process to minimize loss functions, eliminating the need for manual parameter adjustment by engineers while achieving optimal processing performance.
Solution Approach 2:
The patent transforms fixed manual parameters into learnable model parameters. Instead of requiring engineers to set denoising strength, demosaicing filters, and color correction matrices manually, the system uses trainable parameters that are automatically optimized during training, allowing the model to adapt to different imaging conditions and sensor characteristics.
3Manufacturing precision
If separate ISP modules are used for different processing stages, then each module can be optimized independently, but consistency between modules becomes difficult to achieve
Solution Approach 1:
By merging all ISP processing stages into a single neural network, the patent ensures that all processing operations work together consistently. The unified model processes data through coordinated layers that maintain global optimization, preventing the inconsistency and artifact propagation that occurs when separate modules process images independently.
Solution Approach 2:
The training process uses feedback through loss functions that compare network output with ground truth images. This feedback mechanism ensures that all processing stages within the network are jointly optimized to produce consistent results, with the model learning to coordinate its internal processing stages to minimize overall error and maintain image fidelity throughout the pipeline.
Data Source
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AI summary
An image processing module configured to implement a multi-part trained artificial intelligence model, wherein the image processing module is configured to: receive an input image; implement a first part of the model to determine a first transformation for the image in a first colour space; apply the first transformation to the image to form a first adjusted image; implement a second part of the model to determine a second transformation for the image in a second colour space; apply the second transformation to the first adjusted image to form a second adjusted image; and output an image derived from the second adjusted image.