Weighted Pixel Binning for Even Spatial Distribution
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Solution Overview
Problem
Conventional imaging systems perform pixel binning, resulting in uneven spatial distribution of pixels in binned images, which affects the representation of the original image.
Innovation Solution
The proposed solution involves a method of pixel binning where pixels are grouped and weighted to determine the spatial location of binned pixels, using specific weight assignments for corner, side, and center pixels to achieve even distribution, as illustrated in FIGS. 5A-5D, and equations 1-4, allowing for the formation of binned images with improved spatial alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pixel binning is performed by grouping pixels, then signal-to-noise ratio is improved in low-light situations, but spatial distribution of pixels in the binned image becomes uneven
Solution Approach 1:
The patent applies local quality by assigning different weights to pixels based on their spatial positions within a binning group. Corner pixels, side pixels, and center pixels are given different weights to compensate for their varying contributions to the binned pixel signal. This weighted approach ensures that pixels at different locations within the group contribute proportionally to the final binned image, achieving uniform spatial distribution while maintaining the signal-to-noise ratio benefits of pixel binning
2Reliability
If pixel binning reduces image resolution, then higher signal-to-noise ratio is achieved, but representation of the original image deteriorates
Solution Approach 1:
The patent changes the parameter of pixel contribution weight based on spatial position. By introducing position-dependent weights (corner, side, center weights), the system optimizes how original pixel information is aggregated into binned pixels. This parameter change ensures that the reduction in resolution does not proportionally reduce the fidelity of the original image representation, as the weighted combination preserves more accurate spatial information
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 generates binned images with evenly distributed pixels, enhancing the representation of the original image and increasing signal-to-noise ratios, particularly in low-light conditions.
Implementation Method 1
Each pixel receives incident photons (light) and converts the photons into electrical signals
Data Source
AI summary
An imager may be configured to perform pixel binning on captured images. Pixel binning may be performed by forming groups of pixels and combining sampled values from the pixels in each group. The sampled values from the pixels may be combined by assigning weights to each pixel, scaling the sampled values by the corresponding weights, and summing the scaled values. The groups of pixels and pixel weights may be selected to produce binned images with even spatial distribution. The pixel binning operation may be performed by processing circuitry that receives captured image data from the imager. The pixel binning operation may also be separated into a horizontal binning step and a vertical binning step that are performed by image readout circuitry during image readout. During the horizontal binning step, pixels of a particular row may be combined. During the vertical binning step, pixel rows may be combined.