Infrared image dynamic detail enhancement method

Abstract

The invention discloses an infrared image detail enhancement method, which comprises the following seven steps of: extracting an original image, eliminating noise, calculating gradient information, calculating weight, performing zero elimination on the weight, equalizing a histogram and outputting the image. In the method, the noise is eliminated by a method of comparing with surrounding eight points, the gradient information of the image is acquired by using the difference of image expansion and corrosion in a mathematical morphologic method, the gradient information is corrected so as to prevent the pixel weight contribution from being zero, the gray statistical weight is calculated by combining a gray value of the denoised image, and the weight is applied to a histogram equalization algorithm for a detailed image and is combined with the detailed information of the image so as to finally acquire an image subjected to detail enhancement.

Description

technical field [0001] The invention relates to the technical field of infrared image processing, in particular to a method for enhancing dynamic details of an infrared image. Background technique [0002] The infrared light emitted by different types of objects has its own specific band, and people can use this specific band of infrared light to realize the detection and tracking of objects and targets. Since the development of lead sulfide (PbS) detectors in Germany in the early 1940s, infrared detection technology has made great progress. Cooled infrared detectors represented by photon detection have been used in many fields such as communication, medicine, military and industry. Large-scale promotion and application. The invention of microbolometer as a new type of infrared focal plane array has effectively promoted the development of uncooled infrared detectors. Its advantages of high reliability, miniaturization, and low power consumption will make uncooled infrared ...

AI Technical Summary

Problems solved by technology

[0005] In actual detection, limited by the performance of the detector, the infrared image will have many problems such as low contrast, indistinct detail information, and low signal-to-noise ratio. level, the vast majority of pixel grayscales are concentrated in some grayscale levels, and peaks appear in the histogram

Traditional infrared image enhancement methods need to take into account various factors such as dynamic range, noise suppression, processor and storage resources, and have high requirements for algorithms, such as histogram equalization, including HE (Histeq Equalization), PE (Plateau Equalization) and Various linear stretching algorithms, but when processing high dynamic range infrared images, they cannot take into account the details of the low-temperature and high-temperature regions of the image at the same time, which may cause the loss of image detail information of target objects that are not much different from the background temperature

In recent years, other algorithms improved by the histogram equalization algorithm have also emerged in an endless stream. For example, two-way histogram equalization is to divide the gray level of the histogram into equal intervals again on the basis of HE, so as to obtain continuous gray images. ; The wavelet transform method can enhance the contrast of the infrared image while suppressing various noises in the infrared image well; based on the fuzzy analysis method, an infrared image enhancement algorithm based on fuzzy logic is proposed, but these methods are not It can only overcome the defects in one aspect, and cannot meet the requirements for clarity and detailed information in both macro and micro aspects

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