Image self-adaptive digital quantification method and structure

Abstract

The invention provides an image self-adaptive digital quantification structure which comprises a two-value capacity discharge digital quantification curve generating module, a histogram statistic module, a histogram threshold value parameter acquiring module, a histogram threshold value parameter correcting module and a gray value calculating module. The image contrasts continuously acquired in real time are self-adaptively regulated on the basis of an ASIC (application specific integrated circuit) streamline structure, then images acquired through different dry, wet and shallow medium contact surfaces are excellent in contract uniformity, the processing speed is optimized, and the digital image which is regulated through same frame contrast is output while a frame image analog signal is acquired. According to the invention, full custom ASIC achieves image self-adaptive digital quantification, a streamline structure of processing the image while acquiring the image is conducted, the processing speed is high, the cost is low, and the power consumption is low.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image self-adaptive digital quantization method and structure suitable for continuous acquisition of capacitive image acquisition arrays. Background technique [0002] There are various types of capacitive image acquisition arrays, and the applications in fingerprints include: scratch-type fingerprint sensors, capacitive area-array fingerprint sensors, etc. Each capacitance acquisition unit on the capacitance acquisition array corresponds to a pixel of the acquired image. The image acquisition method of this type of sensor is based on the capacitance discharge of a certain capacitance acquisition unit on the capacitive image acquisition array. According to the capacitance discharge curve (the relationship between voltage change and discharge times), it is converted into image gray value by traditional ADC to realize the image collection. The traditional ADC is used to digital...

AI Technical Summary

Problems solved by technology

[0003] The existing capacitance acquisition technology cannot produce the same capacitance discharge curve for different types of media contact surfaces, such as: dry and wet contact surfaces, which makes the analog capacitance discharge curve processed by the traditional ADC. The digital capacitance discharge curve also produces differences. At the same time, the traditional ADC itself has the problem of difference in analog-to-digital conversion accuracy, so that the original image obtained by capacitive acquisition has a very different contrast, and in order to adjust the contrast of the image, a general-purpose processor such as DSP has to be introduced to it. Since most DSPs are based on serial instruction sets, the images often need to be cached during the processing process, and the processing speed is slow, and the DSP based on the parallel processing architecture is expensive, which increases the cost of the entire image acquisition device, and at the same time, the parallel processing degree of the DSP is not The optimization based on a special function requirement cannot be achieved, and the parallel processing speed with the highest degree of optimization in theory cannot be achieved

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