Neural Network Image Signal Processing for Raw Patch Reconstruction
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Solution Overview
Problem
Traditional image signal processors (ISPs) require extensive hand-tuning of numerous parameters, leading to time-consuming and expensive support requirements due to their discrete functional blocks for image processing tasks.
Innovation Solution
A machine learning ISP using a neural network system that integrates multiple image processing functions, such as demosaicing and noise reduction, as a single unit, reducing the need for hand-tuned parameters by learning the mapping from raw image data to final output images through training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate processors are used for different image processing functions, then processing functionality is comprehensive, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple image processing functions (noise filtering, edge detection, line detection, shape recognition) into a single processor that shares common resources including the image buffer, processing units, and control logic. This merging approach maintains comprehensive processing functionality while reducing device complexity by eliminating the need for multiple separate processors and their associated duplicate hardware components.
Solution Approach 2:
The processor is designed as a universal device that can perform multiple image processing tasks through different processing units that share common resources. The single processor can execute noise filtering algorithms, edge detection operations, line detection functions, and shape recognition tasks, making it a multi-functional device that replaces what would traditionally require multiple specialized processors.
2Device complexity
If multiple separate processors share common resources, then device complexity is reduced, but processing time increases due to resource sharing conflicts
Solution Approach 1:
The processor employs dynamic resource allocation where the control unit dynamically assigns shared resources (image buffer, processing units) to different processing tasks based on real-time needs. This dynamic scheduling allows the system to minimize waiting time and optimize processing throughput by adapting resource distribution to the current processing pipeline requirements, thereby reducing overall processing time despite resource sharing.
Solution Approach 2:
The processing units operate continuously with pipelined execution, where multiple processing operations are performed in overlapping time periods. The image buffer continuously feeds data to processing units without idle waiting, and completed processing results are immediately fed back for further processing. This continuous operation minimizes downtime and ensures that resource sharing does not significantly delay processing completion.
3Device complexity
If a single processor performs all image processing functions, then device complexity is reduced, but processing precision may deteriorate due to resource constraints
Solution Approach 1:
The processor is segmented into multiple specialized processing units (noise filtering unit, edge detection unit, line detection unit, shape recognition unit) that each handle specific processing tasks. This segmentation allows each unit to be optimized for its specific function while all units share common resources. The control unit coordinates these segmented units to work together, maintaining processing precision through specialized algorithms while achieving lower device complexity through resource sharing.
Data Source
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AI summary
Techniques and systems are provided for processing image data using one or more neural networks. For example, a patch of raw image data can be obtained. The patch can include a subset of pixels of a frame of raw image data, and the frame can be captured using one or more image sensors. The patch of raw image data includes a single color component for each pixel of the subset of pixels. At least one neural network can be applied to the patch of raw image data to determine a plurality of color component values for one or more pixels of the subset of pixels. A patch of output image data can then be generated based on application of the at least one neural network to the patch of raw image data. The patch of output image data includes a subset of pixels of a frame of output image data, and also includes the plurality of color component values for one or more pixels of the subset of pixels of the frame of output image data. Application of the at least one neural network causes the patch of output image data to include fewer pixels than the patch of raw image data. Multiple patches from the frame can be processed by the at least one neural network in order to generate a final output image. In some cases, the patches from the frame can be overlapping so that the final output image contains a complete picture.