Balanced Ring Top-Hat Transformation for Infrared Small-Target Detection With Guided Filter Kernel

Zhu, Hu; Zhang, Jieke; Xu, Guoxia; Deng, Lizhen

doi:10.1109/taes.2020.2982347

Cited by 31 publications

(13 citation statements)

References 32 publications

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“…In (4), Vin is intra-class variance and Vout is inter-class variance. Where Vin is defined as, 1 1 2…”

Section: B Adaptive Bi-interval Histogram Equalization With Detailsmentioning

confidence: 99%

“…Infrared imaging technology is widely used in aerospace, maritime rescue, and military target detection [1][2][3]. However, the contrast of infrared image is low and the texture details are fuzzy.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that the enhanced infrared images have higher contrast, clearer texture detail and more comfortable visual effect. Thus, image enhancement has attracted more and more attention from researchers [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Liu

Pan

2020

IEEE Access

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In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.

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“…In (4), Vin is intra-class variance and Vout is inter-class variance. Where Vin is defined as, 1 1 2…”

Section: B Adaptive Bi-interval Histogram Equalization With Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Liu

Pan

2020

IEEE Access

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“…In morphology, the scale of the structuring element is a critical parameter; its value determines the accuracy of feature extraction [ 19 ] by utilizing the prior knowledge of the size and shape of the target to construct the appropriate structuring elements and then obtain the detection image through the differential operation between the original image and the filtered image, such as in traditional top-hat [ 20 ] and adaptive top-hat methods [ 21 , 22 ]. The structuring element has a great impact on the top-hat operation and the new white top-hat transformation (NWTH) [ 23 ] is often considered the best in the morphological approach, along with some improved ring top-hat transformation [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation

Yuan

Sun

2022

Sensors

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In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).

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“…Actually, road object detection has reached a bottleneck for further improvement because of the small scale of targets [27,28]. Infrared (IR) small object detection has been established [29] recently, as well as the remote sensing radar [30] and LiDAR [31]. Ref.…”

Section: Introductionmentioning

confidence: 99%

A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

Song

Huang

et al. 2021

Applied Sciences

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It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.

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Balanced Ring Top-Hat Transformation for Infrared Small-Target Detection With Guided Filter Kernel

Cited by 31 publications

References 32 publications

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation

A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

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