NSCT domain infrared image enhancement method based on improved Retinex and quantum flora algorithm

Abstract

The invention relates to an NSCT domain infrared image enhancement method based on improved Retinex and a quantum flora algorithm, and the method comprises the steps: carrying out the multi-scale andmulti-direction decomposition of an infrared image through employing NSCT transformation, and obtaining a low-frequency sub-band and a high-frequency sub-band; then, adopting an improved Retinex algorithm to enhance the low-frequency sub-band coefficient; introducing a quantum updating strategy of the nonlinear self-adaptive revolving door into a flora algorithm for optimizing fractional differential parameters, and performing denoising and enhancement on a high-frequency sub-band by combining a Bayes Shrink threshold value; and finally, performing NSCT inverse transformation on the processedlow-frequency sub-band coefficient and high-frequency sub-band coefficient to obtain an enhanced image. According to the method, the contrast, definition and information entropy of the infrared imageare greatly improved, the details of the infrared image are enhanced, and a more favorable infrared enhanced image is provided for subsequent infrared image processing.

Description

technical field [0001] The invention relates to the technical field of infrared image enhancement, in particular to an infrared image enhancement method in NSCT domain based on improved Retinex and quantum flora algorithm. Background technique [0002] Infrared technology has the advantages of strong anti-interference ability, strong environmental adaptability, good concealment and strong recognition ability, and is widely used in military investigation, medical diagnosis, industrial detection and resource exploration and other fields. Due to the complex imaging environment and the limitations of the inherent characteristics of the detector itself, infrared images generally have defects such as low contrast, blurred edges, and low signal-to-noise ratio, making some image details difficult to detect by human eyes. At this time, it is necessary to use image enhancement technology to process the infrared image, which can improve the visual effect of the infrared image and prepa...

AI Technical Summary

Problems solved by technology

The Wavelet transform can extract image information from multiple scales, but its directionality is limited, and it can only describe image details from the vertical and horizontal directions. It cannot effectively capture the edge details of the image, and it is easy to cause the loss of weak details.

The Contourlet transform can decompose in any direction on any scale, and it uses the "strip" support interval to represent the contour and directional texture feature information of the image, but the downsampling process will inevitably produce spectral aliasing Distortion phenomenon, and then the decomposition of the image will be greatly reduced

Regarding the enhancement algorithm based on non-subsampling Contourlet transform, combined with histogram equalization, it can effectively improve the overall effect of the image, and at the same time suppress the amplification of noise, but it is not ideal for local enhancement.

[0004] In summary, the existing infrared image enhancement methods in the time domain improve the contrast of infrared images, but it is easy to cause loss of details, poor noise processing, and over-enhancement.

The enhancement method in the frequency domain improves the edge texture and brightness uniformity of the infrared image, but it is prone to partial edge distortion and noise amplification.

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