A Local Contrast Method for Small Infrared Target Detection

By C. L. Philip Chen, Hong Li, Yantao Wei, Tian Xia, Yuanyan Tang. IEEE Transactions on Geoscience and Remote Sensing, 2014

1,213 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/2985d0416996d99c4aa16bf2e052992b22532c9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Zhenming Peng
2. Landan Zhang
3. Kobus Barnard
4. Fei Zhou
5. Jocelyn Chanussot
6. Danfeng Hong
7. Xin Wu
8. Siying Cao

In Electrical and Electronic Engineering

1. Zhenming Peng
2. Kobus Barnard
3. Fei Zhou
4. Landan Zhang
5. Yimian Dai
6. Yiquan Wu
7. Wei An
8. Jocelyn Chanussot

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Xin Wu
4. Kobus Barnard
5. Yulan Guo
6. Fei Zhou
7. Longguang Wang
8. Lei Wang

Citations in the corpus

• A Robust Infrared Small Target Detection Algorithm Based on Human Visual System (2014, methodology): “[11] gave a new contrast measuring method named local contrast measure (LCM), all the three algorithms can improve the detection rate, but the LoG filter is hard to calculate and LCM is calculated pixel by pixel, which are both time consuming.”
• ISNet: Shape Matters for Infrared Small Target Detection (2022, methodology): “Representative methods include HVS based methods, such as tri-layer local contrast measure (TLLCM) [4] and weighted strengthened local contrast measure (WSLCM) [12], filtering based methods [2, 9], as well as low-rank based methods, such as reweighted infrared patch-tensor (RIPT) [5], partial sum of”
• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, background): “Table III lists the detection accuracy of nine infrared detection methods, including infrared patch image (IPI) [7], local contrast measure (LCM) [8], reweighted infrared patch-tensor (RIPT) [14], multiscale gray difference weighted image entropy (MGDWIE) [11], nonnegativity-constrained variational ”
• Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018, methodology): “The ratio form definitions, including the local contrast measure (LCM) [10], the improved local contrast measure (ILCM) [4], the novel local contrast measure (NLCM) [11], and the weighted local difference measure (WLDM) [12], first calculate the ratio between the local center and the local surroundi”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “[6], [9], local-contrast-based methods [10], [11], [12], [13],”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, background): “…[7], [8]; 2) human visual system-based approaches assume that the saliency of a target is only re-lated to the local contrast of its surroundings [9], [10], [11], [12]; 3) optimization-based approaches transform the target detection task into a sparse low-rank tensor decomposition problem [13],…”
• Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm (2019, methodology): “Up to now, the consistency of backgrounds [12–15], the saliency of targets [16–19], the sparsity of targets and the low rank of backgrounds [20–24] are the most used assumptions to detect infrared small targets in single image from different perspectives.”
• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017, background): “contrast measure (LCM) [17], improved local contrast measure (ILCM) [18], multiscale patch-based contrast measure (MPCM) [19], multiscale gray difference weighted image entropy [20], improved difference of Gabors (IDoGb) [21], local saliency map (LSM) [22], weighted local difference measure (WLDM) [”
• Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2, 1 Norm (2018, methodology): “To address such problems, the authors proposed a weighted IPI (WIPI) model via the column-wise weights.”
• Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images (2019, methodology): “A common pipeline is to apply image filtering [12, 29, 3], contrast and saliency detection [4, 9, 11] or low-rank recovery [13, 8, 7] to suppress the background and enhance the objects to obtain a confidence map.”
• A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window (2020, methodology): “For example, Chen et al. [14] proposed the local contrast measure (LCM), and it used a nested structure with eight directions; Han et al. [6] used an average operation to suppress PNHB; Qin and Li [15] indicated that only some maximal pixels should be considered during averaging; Wei et al. [16]…”
• Attentional Local Contrast Networks for Infrared Small Target Detection (2020, methodology): “Specially, we will deal with the challenges: 1) how to get rid of the patch-based paradigm of local contrast measure [8] which cannot fit into an end-to-end network and 2) how to measure the local contrast on feature maps fast.”
• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, methodology): “For model-driven methods, we choose eleven methods including top-hat filter [1], local contrast method (LCM) [2],”