(Retracted) Infrared image filtering and enhancement processing method based upon image processing technology

Abstract

Due to the processing method used in many existing image enhancement algorithms, some inherent noise in the image is amplified when the image is enhanced globally or locally. The quality of infrared images directly affects the wide application of infrared imaging technology, yet the quality of infrared image depends largely on the advanced nature and correct application of infrared image processing technology (IPT). This research mainly discusses the infrared image filtering enhancement processing method based on IPT. We briefly summarize the research significance, application fields and research status of the image registration and enhancement. We summarize the commonly used methods. We use a contrast saliency filter to obtain the region of interest and perform statistical segmentation on the region to make it more suitable for observation and subsequent processing. No matter what the background environment is, the image detail enhancement algorithm enhances all the details in the image in a balanced manner. Therefore, we adopt the scheme of separating the background and the details to avoid the influence of the background difference on the detail display effect in other enhancement methods. Objective experiments have also proved that it is difficult to achieve consistency between subjective and objective evaluation of image quality. Therefore, the evaluation of processed image quality still needs to combine subjective visual effects and objective evaluation. We use optimized hierarchical differential expression theory to solve the acquired saliency region and obtain the differential vector that realizes the gray difference amplification of this region. The dynamic range of the original image is very narrow, roughly concentrated in [0, 50]. The gray range of the image processed by the enhancement algorithm has been well expanded, basically covering [0, 255]. The proposed infrared image enhancement method achieves a better visual enhancement effect.

Hierarchical Feature Enhancement Algorithm for Multispectral Infrared Images of Dark and Weak Targets

“…This paper uses a bilateral filtering algorithm to better maintain the edge information of the image target, estimate the target pixel gray value by using the spatial proximity function as the spatial domain weight factor, ensure the target image flat area gray value information, and strengthen the target edge information to achieve image smoothing and noise removal performance. The histogram equalization enhancement algorithm is used to enhance the contrast between target and background pixels in the image and improve the dynamic difference of the gray value between pixels in the image [12,13]. Finally, the enhanced image is sharpened by gamma transformation and Laplace to make the linear response of the image exposure intensity closer to the response perceived by the human eye and to improve the image contrast.…”