Image Signal Processor Adaptive Noise Reduction
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Solution Overview
Problem
Traditional image signal processors (ISPs) require extensive hand-tuning of over 10,000 pre-tuned parameters, making them time-consuming and expensive to re-tune for different customer preferences, and they often use static tuning settings for processing images, which limits their adaptability to varying lighting conditions.
Innovation Solution
The implementation of a machine learning-based apparatus and method for processing frame data, which performs noise reduction operations using feedback data to adaptively improve image quality without the need for extensive re-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ISPs use discrete functional blocks with pre-tuned parameters, then image processing can be performed with established settings, but the system requires extensive hand-tuning of over 10,000 parameters which is time-consuming and expensive
Solution Approach 1:
The patent implements feedback loops where processed frame data is fed back into the machine learning component to generate feedback data, which is then used to iteratively refine noise reduction parameters and improve processing quality automatically without manual intervention
Solution Approach 2:
The machine learning component performs self-tuning by automatically adjusting its own parameters based on feedback data from processed frames, eliminating the need for external hand-tuning of thousands of parameters while maintaining reliable image processing quality
2Adaptability or versatility
If traditional ISPs use static tuning settings, then the system is simpler to operate, but it has limited adaptability to varying lighting conditions and customer preferences
Solution Approach 1:
The patent transforms static tuning settings into dynamic adaptive settings by using machine learning components that continuously adjust noise reduction parameters based on feedback data from processed frames, enabling the system to adapt to varying lighting conditions and scene characteristics in real-time
Solution Approach 2:
The system automatically changes processing parameters based on analysis of feedback data, allowing the noise reduction strength and other parameters to vary dynamically according to the specific characteristics of each frame and lighting condition rather than using fixed static settings
3Manufacturing precision
If traditional ISPs perform noise reduction with fixed parameters, then processing is faster and simpler, but noise reduction effectiveness is limited and cannot be optimized for different scenarios
Solution Approach 1:
The patent uses feedback loops where the output of noise reduction processing is analyzed and fed back to automatically adjust parameters for subsequent processing, enabling continuous optimization of noise reduction precision through automated parameter refinement based on actual processing results
Solution Approach 2:
The patent replaces manual mechanical tuning of parameters with an automated machine learning system that uses feedback data to automatically optimize noise reduction parameters, substituting human-operated adjustment mechanisms with intelligent automated control
Data Source
AI summary
The present disclosure generally relates to image processing. For example, aspects of the present disclosure include systems and techniques for performing spatial and temporal processing of image data. Certain aspects provide an apparatus for processing frame data. The apparatus generally includes a memory, and one or more processors coupled to the memory, the one or more processors configured to: perform a first noise reduction operation based on first frame data via a machine learning component to generate first processed frame data; generate first feedback data based on the first processed frame data; and perform, via the machine learning component, a second noise reduction operation based on second frame data and the first feedback data.