User-Configurable Wavelet Noise Estimation for Color Filter Arrays
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Solution Overview
Problem
Conventional image processing systems struggle with efficiently processing raw pixel data from image capture devices using arbitrary color filter arrays due to the need for multiple processing blocks, leading to increased silicon area and reduced efficiency, especially in high dynamic range (HDR) imaging.
Innovation Solution
An image processing system employing a high bit-width (HBW) pipeline that includes a front-end processing logic to merge raw pixel data, a noise filter logic to estimate noise using wavelet decomposition and user-configurable functions, and a back-end processing logic for post-processing, effectively handling data from any arbitrary color filter array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image processing systems use multiple processing blocks to handle arbitrary color filter arrays, then processing capability is improved, but silicon area increases and efficiency decreases
Solution Approach 1:
The patent implements a universal processing pipeline that can handle any arbitrary color filter array configuration through a single integrated block. The system uses configurable correlation information and user-programmable functions to adapt to different CFA patterns (Bayer, X-Trans, Foveon, etc.) without requiring separate dedicated processing blocks for each type, thereby reducing silicon area while maintaining processing capability across multiple formats
Solution Approach 2:
The system changes processing parameters dynamically based on the input CFA type. It uses user-configurable correlation information and noise-intensity functions that can be programmed to match different color filter patterns. This parameter-based adaptation allows a single processing block to handle multiple CFA types efficiently, avoiding the need for multiple fixed-architecture blocks
2Adaptability or versatility
If conventional image processing systems use multiple processing blocks, then processing capability is improved, but processing efficiency decreases
Solution Approach 1:
The patent merges multiple separate processing functions into a single integrated high bit-width pipeline. The front-end processing logic, noise filter logic, and back-end processing logic are combined in one continuous data flow path, eliminating the overhead and data transfer delays associated with multiple discrete blocks. This unified architecture maintains versatility while improving processing efficiency through reduced latency and better resource utilization
Solution Approach 2:
The processing pipeline operates continuously with data flowing seamlessly from front-end merging through noise filtering to back-end processing. The unified architecture eliminates idle time and data transfer interruptions that occur in multi-block systems, ensuring continuous useful action throughout the processing chain and thereby improving overall productivity
3Productivity
If noise filtering is performed without user-configurable parameters, then processing speed is improved, but noise estimation accuracy decreases
Solution Approach 1:
The system performs preliminary configuration of noise-intensity functions and correlation information based on user input before actual image processing begins. This pre-programming of parameters allows the processing pipeline to operate at full speed during actual execution while still benefiting from accurate, customized noise estimation. The user-configurable functions are prepared in advance, enabling both speed and accuracy during runtime
Solution Approach 2:
The noise filtering system dynamically adapts to different imaging conditions through user-configurable parameters. The noise-intensity function and correlation information can be adjusted based on specific scene characteristics, lighting conditions, and sensor properties. This dynamic configurability maintains processing speed while improving noise estimation accuracy for diverse imaging scenarios
Data Source
AI summary
In some examples, a method comprises receiving pixel data from an image capture device having a color filter, wherein the pixel data represents a portion of an image. The method further includes performing wavelet decomposition on the pixel data to produce decomposed pixel data and determining a local intensity of the pixel data. The method also includes determining a noise threshold value based on the local intensity and a noise intensity function that is based on the color filter; determining a noise value for the pixel data based on the decomposed pixel data and the noise threshold value; and correcting the pixel data based on the noise value to produce an output image.