An ISAR image self-adaptive quantization method based on histogram entropy

By adopting an adaptive quantization method for ISAR images based on histogram entropy, the problems of image readability and adaptability in the quantization process of ISAR imaging results are solved, and efficient quantization and real-time processing under different conditions are achieved.

CN117491997BActive Publication Date: 2026-07-21CHINA ELECTRONIC TECH GRP CORP NO 38 RES INST
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Patent Information

Application Number
CN202311402766.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-10-25
Publication Date
2026-07-21
Estimated Expiration
2043-10-25

AI Technical Summary

Technical Problem

During the quantization process of ISAR imaging results, the results are easily affected by factors such as target size, target type, and system signal-to-noise ratio, making it difficult to guarantee image readability. Existing methods are inefficient and difficult to adapt to changes in target and radar operating frequency band.

Method used

An adaptive quantization method for ISAR images based on histogram entropy is adopted. By constructing an ISAR amplitude floating-point data matrix, the quantization boundary is determined, and the combination of quantization boundaries is optimized using histogram entropy to achieve adaptive quantization.

Benefits of technology

It improves the readability and adaptability of ISAR images, reduces computational load, is suitable for different targets, resolutions and signal-to-noise ratio environments, and is suitable for real-time processing.

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Abstract

The application discloses an ISAR image adaptive quantization method based on histogram entropy, and belongs to the technical field of radars. An M*N dimension ISAR amplitude floating-point data matrix model in units of dB is firstly constructed; then a quantization boundary value coordinate axis of the ISAR image quantization is constructed; then the optimal upper and lower quantization boundaries of the ISAR amplitude data are obtained; finally, the optimal quantization image is obtained by quantization based on the obtained upper and lower quantization boundaries. The application uses the entropy of the ISAR image as an evaluation index of adaptive quantization, has universal applicability, and reduces the problem of non-uniform quantization of different targets, different resolutions and different signal-to-noise ratio environments during ISAR imaging. By using the histogram entropy of the ISAR image, the calculation amount of the entropy operation of the whole image is avoided, and only the histogram needs to be quantized, so that the operation scale of the two-dimensional image is reduced to the scale of the quantization order W, and the engineering application of real-time processing is more favorable. The histogram entropy can be used for different quantization bit numbers.
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