Title :
A spatially adaptive multi-model denoising strategy for infrared dim small target detection
Author :
Yizhou Ye ; Yunze Cai
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.
Keywords :
Bayes methods; discrete wavelet transforms; image denoising; image reconstruction; object detection; DWT method; GCV thresholding; IR small target detection; SABS thresholding; SAMMDS based IRDST detection method; distribution characteristics; generalized cross validation thresholding; infrared dim small target detection; infrared image; infrared remote sensing; simple global thresholding; spatially adaptive BayesShrink thresholding; spatially adaptive multi-model denoising strategy; thresholding strategy; wavelet coefficients image; Discrete wavelet transforms; Estimation; Noise; Noise reduction; Object detection; Thyristors; adaptive thresholding; infrared small target; multi-model denoising; spatially adaptive; wavelet;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
DOI :
10.1109/ICARCV.2014.7064409