Image Signal Processor Binning With Dual-Pixel Disparity Correction
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Solution Overview
Problem
Existing image sensors suffer from disparity distortion due to varying light incidence on photoelectric conversion elements, leading to image quality degradation.
Innovation Solution
An image signal processor with a disparity correction unit and a disparity processing module that corrects disparity distortion by calculating and applying disparity data to image data, enhancing image resolution through binning analog signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If disparity correction is applied to correct distortion from varying light incidence, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent applies disparity correction in advance during the image signal processing stage, before final image output. By calculating and applying disparity compensation data early in the processing pipeline, the system prevents distortion from propagating through subsequent processing stages, thereby improving overall image quality while managing complexity through proactive correction rather than reactive processing.
Solution Approach 2:
The patent introduces disparity compensation data as an intermediary element that mediates between the raw distorted image data and the final corrected output. This intermediary data structure allows the system to separate the correction calculation from the correction application, enabling modular processing that improves image quality while keeping individual processing stages manageable in complexity.
2Reliability
If binning is applied to enhance image resolution, then signal-to-noise ratio is improved, but spatial resolution is reduced
Solution Approach 1:
The patent applies binning selectively to specific regions of the image sensor array rather than uniformly across all pixels. By identifying regions where signal-to-noise ratio improvement is most beneficial and applying binning locally to those areas, the system enhances signal quality in critical regions while preserving spatial resolution in areas where individual pixel data is more valuable.
Solution Approach 2:
The patent implements dynamic binning where the binning configuration can be adjusted based on scene characteristics, lighting conditions, and focal depth. The system can switch between binned and non-binned modes for different regions or time periods, allowing optimal signal-to-noise ratio enhancement while maintaining spatial resolution flexibility according to the specific imaging requirements.
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
The solution effectively reduces disparity distortion and enhances image resolution by correcting pixel value discrepancies across photoelectric conversion elements.
Implementation Method 1
each of the pixels including at least a first photoelectric conversion element and a second photoelectric conversion element
Data Source
AI summary
An image signal processor includes a register and a disparity correction unit. The register stores disparity data obtained from a pattern image data that an image senor generates, and the image sensor includes a plurality of pixels, and each of the pixel includes at least a first photoelectric conversion element and a second photoelectric conversion element. The image sensor generates the pattern image data in response to a pattern image located at a first distance from the image sensor. The disparity correction unit corrects a disparity distortion of an image data based on the disparity data to generate a result image data, and the image senor generates the image data by capturing an object.