Image Signal Processor Shot Noise Cancellation
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Solution Overview
Problem
Digital image sensors face limitations in quality due to structural and cost constraints, resulting in noisy image signals that existing processing techniques struggle to effectively address.
Innovation Solution
An image signal processor that receives Bayer image signals, performs shot noise cancellation and bad pixel correction using adaptive threshold values, and applies interpolation to generate normal image signals, incorporating a memory for storing reference brightness values and variation values, and utilizing strong or weak filters based on calculated flatness and brightness differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital image sensors are used to capture images, then image capture capability is improved, but image quality deteriorates due to noise from structural and cost limitations
Solution Approach 1:
The patent applies preliminary action by performing shot noise cancellation and bad pixel correction on Bayer image signals before the interpolation process. The shot noise cancelation unit processes the raw Bayer signals to remove noise components, and the bad pixel correction unit repairs defective pixels, both before the normal image signal is generated through interpolation. This preliminary processing ensures that noise and defects do not propagate through subsequent image processing stages, thereby improving final image quality while maintaining the benefits of digital image sensor capture.
2Device complexity
If existing processing techniques are applied to noisy image signals, then processing simplicity is maintained, but noise reduction effectiveness deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the image signal processing into distinct functional units: a shot noise cancelation unit that processes Bayer signals to remove shot noise, a bad pixel correction unit that identifies and corrects defective pixels, and an interpolation unit that generates normal image signals. Each unit performs a specific noise reduction function with dedicated processing logic, allowing complex noise reduction to be achieved through modular, organized stages rather than a single complex processor.
3Measurement precision
If adaptive threshold values are calculated and filters are applied, then noise reduction precision is improved, but processing complexity increases
Solution Approach 1:
The patent applies dynamics by implementing adaptive threshold calculation that adjusts processing parameters based on local image characteristics. The shot noise cancelation unit and bad pixel correction unit calculate thresholds dynamically according to the statistical properties of the Bayer image signal in different regions. This allows the processing to adapt to varying noise levels and image content, improving noise reduction precision while maintaining reasonable processing complexity through algorithmic adaptability rather than hardware complexity.
Data Source
AI summary
An image signal processor receives a Bayer image signal from an image sensor and converts the Bayer image signal into a normal image signal. The image signal processor includes a memory configured to store a table including reference brightness values and variation values according to the reference brightness values; a shot noise cancelation unit configured to calculate a reference brightness value of the Bayer image signal, select a variation value in the table of the memory according to the calculated reference brightness value, and perform shot noise cancelling on the Bayer image signal based on the selected variation value to generate a modified Bayer image signal; and an interpolation unit configured to generate the normal image signal by performing interpolation based on the modified Bayer image signal.