Infrared Small Target Detection with Total Variation and Reweighted l1 Regularization

Infrared small target detection plays an important role in infrared search and tracking systems applications. It is difficult to perform target detection when only a single image with complex background clutters and noise is available, where the key is to suppress the complex background clutters and noise while enhancing the small target. In this paper, we propose a novel model for separating the background from the small target based on nonlocal self-similarity for infrared patch-image. A total variation-based regularization term for the small target image is incorporated into the model to suppress the residual background clutters and noise while enhancing the smoothness of the solution. Furthermore, a reweighted sparse constraint is imposed for the small target image to remove the nontarget points while better highlighting the small target. For higher computational efficiency, an adapted version of the alternating direction method of multipliers is employed to solve the resulting minimization problem. Comparative experiments with synthetic and real data demonstrate that the proposed method is superior in detection performance to the state-of-the-art methods in terms of both objective measure and visual quality.

[1]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[2]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[3]  Tianxu Zhang,et al.  Detection of Sea Surface Small Targets in Infrared Images Based on Multilevel Filter and Minimum Risk Bayes Test , 2000, Int. J. Pattern Recognit. Artif. Intell..

[4]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[5]  Xiangzhi Bai,et al.  Analysis of new top-hat transformation and the application for infrared dim small target detection , 2010, Pattern Recognit..

[6]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[7]  Timothy J. Schmit,et al.  Evaluation of radiative transfer models in atmospheric profiling with broadband infrared radiance measurements , 2011 .

[8]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[9]  Houzhang Fang,et al.  Blind image deconvolution with spatially adaptive total variation regularization. , 2012, Optics letters.

[10]  Yi Chang,et al.  Robust destriping method with unidirectional total variation and framelet regularization. , 2013, Optics express.

[11]  Zijun Feng,et al.  Infrared Target Detection and Location for Visual Surveillance Using Fusion Scheme of Visible and Infrared Images , 2013 .

[12]  Yi Yang,et al.  Infrared Patch-Image Model for Small Target Detection in a Single Image , 2013, IEEE Transactions on Image Processing.

[13]  Yi Chang,et al.  Blind Poissonian images deconvolution with framelet regularization. , 2013, Optics letters.

[14]  Yuan Yan Tang,et al.  A Local Contrast Method for Small Infrared Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Dongchen Li,et al.  Tri-feature-based detection of floating small targets in sea clutter , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Lei Zhang,et al.  Robust Principal Component Analysis with Complex Noise , 2014, ICML.

[17]  Lin Sun,et al.  Extended target detection amid clutter suppression using the combination of the sparsity and total variation , 2016, 2016 CIE International Conference on Radar (RADAR).

[18]  Jun Huang,et al.  An Infrared Small Target Detecting Algorithm Based on Human Visual System , 2016, IEEE Geoscience and Remote Sensing Letters.

[19]  Xin Zhou,et al.  Infrared small-target detection using multiscale gray difference weighted image entropy , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Huixin Zhou,et al.  Small target detection in infrared image using convolutional neural networks , 2017, Applied Optics and Photonics China.

[21]  Sungho Kim,et al.  Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition , 2017 .

[22]  Dehui Kong,et al.  Infrared dim target detection based on total variation regularization and principal component pursuit , 2017, Image Vis. Comput..

[23]  Ming Zhao,et al.  Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Zhenming Peng,et al.  Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image , 2017 .

[25]  Deyu Meng,et al.  Infrared small-dim target detection based on Markov random field guided noise modeling , 2018, Pattern Recognit..

[26]  Lei Xiong,et al.  Dim infrared image enhancement based on convolutional neural network , 2018, Neurocomputing.

[27]  Yiquan Wu,et al.  Small target detection based on reweighted infrared patch-image model , 2018, IET Image Process..

[28]  Wei An,et al.  Infrared Patch-Tensor Model With Weighted Tensor Nuclear Norm for Small Target Detection in a Single Frame , 2018, IEEE Access.

[29]  Zhenming Peng,et al.  Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2, 1 Norm , 2018, Remote. Sens..

[30]  Xavier Maldague,et al.  Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking , 2018, Remote. Sens..

[31]  Sajid Javed,et al.  On the Applications of Robust PCA in Image and Video Processing , 2018, Proceedings of the IEEE.

[32]  Junhwan Ryu,et al.  Small infrared target detection by data-driven proposal and deep learning-based classification , 2018, Defense + Security.

[33]  Lili Dong,et al.  Infrared target detection in backlighting maritime environment based on visual attention model , 2019, Infrared Physics & Technology.

[34]  Yi Chang,et al.  A Coarse-to-Fine Method for Infrared Small Target Detection , 2019, IEEE Geoscience and Remote Sensing Letters.

[35]  Hao Wu,et al.  Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint , 2019, Remote. Sens..

[36]  Kai Zhang,et al.  Aerial Infrared Target Tracking in Complex Background Based on Combined Tracking and Detecting , 2019, Mathematical Problems in Engineering.