Spatial and temporal bilateral filter for infrared small target enhancement

Bae, Tae-Wuk

doi:10.1016/j.infrared.2013.12.007

Cited by 49 publications

(19 citation statements)

References 27 publications

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“…By combining spatial and temporal information together, a target detection method was introduced using spatial bilateral filter and temporal cross product, which are respectively used to extract the spatial target information and the features of temporal profiles [ 6 ]. Subsequently, a spatial-temporal bilateral filter was presented to detect target trajectories, by extracting spatial and temporal target information simultaneously [ 7 ]. As discussed above, pixel-wise detection methods use a local region or a temporal profile to extract target information under the assumption that the target location has conspicuous discontinuity with the nearby background.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the movement (jitter) of the infrared observation platform or the change of the imaging background, it is difficult to obtain the accurate infrared background by sequential detection methods [ 5 , 6 , 7 ], because the infrared small targets are easily mistaken for background and vice versa. In this case, the single frame detection methods have received a great attention recently, and are valid for infrared small target detection with static or changing backgrounds [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Gao

Wen

Liu

2017

Sensors

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In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Gao

Wen

Liu

2017

Sensors

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“…Infrared (IR) imaging is widely used in military, medical, and industrial field since it is able to create a visual with otherwise non-visible wavelength band to the human eye [1,2]. Recently, IR imaging systems, such as IR search and track (IRST), IR countermeasures (IRCM), and directional IR countermeasure (DIRCM) etc, have been used for developing and evaluating military IR imaging systems.…”

Section: Introductionmentioning

confidence: 99%

IR-Band Conversion of Target and Background Using Surface Temperature Estimation and Error Compensation for Military IR Sensor Simulation

Bae

Kim²,

Ahn

2019

Sensors

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Military infrared (IR) imaging systems utilize one or more IR wavelength-bands, among short wavelength IR (SWIR), middle wavelength IR (MWIR), and long wavelength IR (LWIR) band. The IR image wavelength-band conversion which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image is needed for IR signature modeling and image synthesis in the IR systems. However, the IR wavelength-band conversion is very challenging because absorptivity and transmittance of objects and background (atmosphere) are different according to the IR wavelength band and because radiation and reflectance characteristics of the SWIR are very different from the LWIR and MWIR. Therefore, the IR wavelength-band conversion in this paper applies to only IR targets and monotonous backgrounds at a long distance for military purposes. This paper proposes an IR wavelength-band conversion method which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image by using the surface temperature estimation of an object and the error attenuation method for the estimated temperature. The surface temperature of the object is estimated by an approximated Planck’s radiation equation and the error of estimated temperature is corrected by using the slope information of exact radiance along with the approximated one. The corrected surface temperature is used for generating another IR wavelength-band image. The verification of the proposed method is demonstrated through the simulations using actual IR images obtained by thermal equipment.

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“…Wavelet based algorithms [14] design a group of filters which are matched to a point spread function (PSF) at different scales by choosing a mother wavelet similar to PSF. Unfortunately, they are quite time-consuming and the false alarm rates are always high [15].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

Wan

Ren

et al. 2017

Applied Sciences

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Abstract:In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the salient regions of the original image is obtained by tuning its amplitude spectrum in the frequency domain. Then, a saliency histogram is constructed by means of averaging the accumulated saliency value of each gray level in the map, through which bins corresponding to bright target and dark target are assigned with large values in the histogram. Next, single-frame detection of candidate targets is accomplished by a binarized segmentation using an adaptive threshold, and their centroid coordinates with sub-pixel accuracy are calculated through a connected components labeling method as well as a gray-weighted criterion. Finally, considering the motion characteristics in consecutive frames, an inter-frame false alarm suppression method based on geometrical invariability is developed to improve the precision rate further. Quantitative analyses demonstrate the detecting precision of this proposed approach can be up to 97% and Receiver Operating Characteristic (ROC) curves further verify our method outperforms other state-of-the-arts methods in both detection rate and false alarm rate.

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Spatial and temporal bilateral filter for infrared small target enhancement

Cited by 49 publications

References 27 publications

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

Robust Small Target Co-Detection from Airborne Infrared Image Sequences

IR-Band Conversion of Target and Background Using Surface Temperature Estimation and Error Compensation for Military IR Sensor Simulation

Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability

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