Pixel Binning for Dynamic Range and Resolution Trade-off
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Solution Overview
Problem
Existing image acquisition methods often compromise on either resolution or signal-to-noise ratio to improve dynamic range, resulting in reduced image details or increased noise, failing to optimize image quality across all indicators simultaneously.
Innovation Solution
The method employs pixel binning on partial regions of the image sensor based on data characteristics, such as dynamic range and signal-to-noise ratio, to improve image quality locally while maintaining high resolution and reducing noise, thereby enhancing dynamic range and signal-to-noise ratio while preserving image details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If pixel binning is applied to improve dynamic range, then dynamic range is improved, but image resolution is reduced
Solution Approach 1:
The patent applies pixel binning selectively to different regions of the image sensor based on local scene characteristics. Regions with high dynamic range requirements undergo binning to improve dynamic range, while regions requiring high detail preservation maintain original pixel resolution. This local differentiation resolves the contradiction by applying the resolution-reducing technique only where dynamic range improvement is needed, not uniformly across the entire image.
Solution Approach 2:
The image sensor is divided into multiple regions, each processed differently according to its specific requirements. Some regions undergo pixel binning while others maintain full resolution, allowing simultaneous optimization of dynamic range in certain areas and preservation of image details in other areas, thus resolving the trade-off between these two parameters.
2Reliability
If pixel binning is applied to improve signal-to-noise ratio, then signal-to-noise ratio is improved, but image resolution is reduced
Solution Approach 1:
The system identifies regions with poor signal-to-noise ratio and applies pixel binning specifically to those areas to improve signal quality. Regions with already good signal-to-noise ratio and high detail requirements maintain their original pixel structure. This localized approach resolves the contradiction by improving signal-to-noise ratio only where necessary without uniformly reducing image resolution.
3Measurement precision
If full pixel reading is used to maintain image details, then image resolution is maintained, but dynamic range and signal-to-noise ratio are compromised
Solution Approach 1:
The image is segmented into regions requiring high resolution and regions benefiting from binning. By dividing the sensor output into different processing paths, the system maintains full pixel reading for detail-critical regions while applying binning to improve dynamic range in other regions, thus resolving the contradiction between maintaining image details and improving dynamic range.
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 allows for improved image quality by optimizing dynamic range and signal-to-noise ratio locally, ensuring that image details are maintained and noise is reduced, resulting in enhanced image quality that meets specific requirements without visible region boundaries.
Implementation Method 1
the CCD or CMOS converts the light into an electrical signal
Data Source
AI summary
Image processing technologies, an image acquisition method and an image acquisition apparatus are provided. A method comprises exposing an image sensor, reading a charge on the image sensor and performing analog-to-digital conversion, where a charge of pixels on a partial region of the photosensitive sensor is read by means of pixel binning according to data characteristics of an image of a target scene, and obtaining a target image of the target scene according to the read charge. Local combination can be performed on at least two images of different image quality according to data characteristics of the images, so that the dynamic range and/or signal-to-noise ratio can be improved locally on the basis of presenting image details as fully as possible, that is, the image quality can be improved according to requirements.