A Robust Infrared Small Target Detection Algorithm Based on Human Visual System

By Jinhui Han, Yong Ma, Bo Zhou, Fan Fan, Kun Liang, Yu Fang. IEEE Geoscience and Remote Sensing Letters, 2014

486 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ophthalmology

View Paper: https://www.semanticscholar.org/paper/cf7a38ea3775bda8418b9199f130211b8fcabcc7

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. Wei An
2. Kobus Barnard
3. Fei Zhou
4. Jungang Yang
5. Yang Sun
6. Yulan Guo
7. Chao Xiao
8. Longguang Wang

In Computer Vision and Pattern Recognition

1. Jungang Yang
2. Kobus Barnard
3. Yulan Guo
4. Fei Zhou
5. Longguang Wang
6. Yang Sun
7. Yimian Dai
8. Wei An

In Ophthalmology

1. Kobus Barnard
2. Fei Zhou
3. Jinhui Han
4. Yimian Dai
5. Yiquan Wu

Citations in the corpus

• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, methodology): “Modulation Scheme FPN as Host Network U-Net as Host Network IoU nIoU IoU nIoU b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 TopDownLocal 0.595 0.648 0.693 0.713 0.635 0.662 0.688 0.703 0.648 0.710 0.713 0.718 0.673 0.692 0.694 0.697 BiGlobal 0.599 0.”
• Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018, methodology): “(8) 4) For a PNHB, even its gray value is close to or slightly larger than real target, and its Imean0 will be still smaller than a real target’s when we set K1 to a value larger than 1, so RLCMT > RLCMPNHB. (9) From the discussions above, we can get the conclusion that after RLCM calculation, true ”
• A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window (2020, methodology): “For example, the LCM just uses the maximal value of the central cell, and it is sensitive to PNHB; the improved LCM (ILCM) [6] adopts the mean value of the central cell to suppress PNHB, but the small target may be smoothed too; the novel LCM (NLCM) [15] and the relative LCM (RLCM) [17] only…”
• Infrared Dim and Small Target Detection via Multiple Subspace Learning and Spatial-Temporal Patch-Tensor Model (2021, methodology): “HVS methods [16]–[18] use the contrast of the intensity between the target and local background region to detect the target, but their performance is not robust to varying target size and background clutters.”
• Attentional Local Contrast Networks for Infrared Small Target Detection (2020, background): “For this purpose, different variants of local contrast measures have been proposed to deal with pixel pulse noise [21] and edges [20].”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “[6], [9], local-contrast-based methods [10], [11], [12], [13],”
• 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) [”