Image Signal Processor Noise Level Training Data Selection
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Solution Overview
Problem
Existing deep learning systems for image processing do not effectively consider the output features of image sensors, leading to inefficiencies in processing speed and accuracy as pixel quantities increase.
Innovation Solution
An image signal processor that utilizes deep learning technology, incorporating a pixel array, readout circuit, and image signal processor to perform deep learning-based image processing, with a noise level estimator selecting training data from multiple sets based on the noise level of the image data to optimize processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel quantity of image sensor is increased, then image quality is improved, but processing speed and accuracy becomes more difficult to maintain
Solution Approach 1:
The patent segments the training data into multiple sets, each optimized for specific noise levels or imaging conditions. The image signal processor selectively applies different training data sets based on the current imaging conditions, allowing efficient processing of high-resolution images without requiring a single comprehensive processing model that would be computationally expensive.
Solution Approach 2:
The system dynamically selects which training data set to use based on real-time noise level estimation and imaging conditions. This dynamic adaptation allows the processor to optimize processing speed and accuracy by matching the computational complexity of the processing model to the actual image quality requirements, rather than always using the most complex model.
2Measurement precision
If deep learning processing is applied to all image data, then image quality is improved, but processing efficiency decreases due to not considering output features
Solution Approach 1:
The patent applies different processing approaches to different regions or characteristics of the image data based on local noise levels and imaging conditions. By estimating noise levels and selecting appropriate training data sets for specific conditions, the system applies processing quality locally rather than uniformly across all image data, improving overall efficiency.
Solution Approach 2:
The system changes processing parameters by selecting different training data sets based on noise level estimates and imaging conditions. This parameter adaptation allows the processor to optimize the balance between image quality improvement and processing efficiency by adjusting the complexity and type of deep learning processing applied to each image or image region.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances image processing efficiency by adapting to noise levels and patterns, improving processing speed and accuracy by selectively using training data tailored to the image sensor's output modes, thereby improving image quality and reducing noise.
Implementation Method 1
a pixel array configured to convert received optical signals into electrical signals
Data Source
AI summary
An image signal processor and an image sensor including the same are disclosed. An image sensor includes a pixel array configured to convert received optical signals into electrical signals, a readout circuit configured to convert the electrical signals into image data and output the image data, and an image signal processor configured to perform deep learning-based image processing on the image data based on training data selected from among first training data and second training data based on a noise level of the image data.