Infrared Small Target Detection Based on Multi-scale Block Discrete Cosine Transform

DOI: https://doi.org/10.62517/jike.202404311

Author(s)

Tianmei Dou

Affiliation(s)

Xi'an Fanyi University, Xi'an, Shaanxi, China

Abstract

Due to the variable target size and complex detection environment, small target detection for infrared images may be challenging for individual target analysis, making it easier to overlook alarm or false alarm issues. This study proposes an infrared small target detection technique based on multi-scale chunking discrete cosine transform, combining different characteristics of infrared images in the spatial and frequency domains. First, to improve the correlation between pixels, the multi-scale image-chunking model is applied in the spatial domain to chunk the image at various scales. Next, the chunked image goes through varying degrees of chunked discrete cosine transform reduction to eliminate background information by filtering the low frequency coefficients. Then the small target images is reconstructed to obtain after different scales of frequency filtering. Lastly, multi-scale fusion is carried out to produce the final small target detection outcomes.

Keywords

Image Patching; Discrete Cosine Transform; Image Reconstruction; Object Detection

References

[1] Cai Y, Lin Z, Zhou Y. Morphology Filter for Infrared Dim and Small Target Background Suppression [J]. Electronic Information Warfare Technology, 2012, 27(6): 38-42. [2] Bai X, Zhou F. Analysis of new top-hat transformation and the application for infrared dim small target detection [J]. Pattern Recognition, 2010,43(6): 2145-2156. [3] Bai Xiangzhi.New class of top-hat transformation to enhance infrared small targets[J].Journal of Electronic Imaging, 2008, 17(3):030501. [4] Chen CLP, Li H, Wei Y, et al. A Local Contrast Method for Small Infrared Target Detection[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 52(1): 574-581. [5] Han J, Moradi S, Faramarzi I, et al. Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure[J]. IEEE Geoscience and Remote Sensing Letters, 2020, PP(99):1-5. [6] Li Guokuan, Peng Jiaxiong. Infrared Imaging Dim Target Detection Based on Wavelet Transform [J]. Journal of Huazhong University of Science and Technology, 2000,(05):69-71. [7] Li Dong. Infrared dim small target detection based on spectral residuals and local covariance [J]. Ship Science and Technology 2023,45(23):139-144. [8] Gao C, Meng D, Yang Y, et al. Infrared Patch-Image Model for Small Target Detection in a Single Image[J]. IEEE Transactions on Image Processing 2013,22 (12) :4996-5009. [9] Wang H, Xin YH. Infrared small target detection based on DT-CWT[J]. Laser and Infrared 2020, 50(9): 1145-1152. [10] Zhang N, Xin YH. Infrared small target detection based on wavelet transform and improved Top‐Hat filter[J]. Laser Infr, 2016, 46(11): 1431-1436. [11] Zhang K, Yang K, Li S, et al. A Difference-Based Local Contrast Method for Infrared Small Target Detection Under Complex Background[J]. IEEE Access, 2019. [12] Sun Q, Li L, Xin YH. Infrared small target detection algorithm based on local multi-scale low rank decomposition[J]. LASER & INFRARED 2019,49(03):369-375. [13] Shao Y, Kang X, Ma M, et al. Robust infrared small target detection with multi-feature fusion[J]. Infrared Physics and Technology, 2024, 139. [14] Yanjun Z, Biyun W, Yunze CAI. Multi-Channel Based on Attention Network for Infrared Small Target Detection[J]. Journal of Shanghai Jiaotong University (Science), 2024, 29(3): 414. [15] Li Jian, Zhao HH, Ma B, et al. PRNU Anonymization Algorithm Based on DCT and Wiener Filtering[J]. Forensic Science and Technology 2024,49(04):350-358.