Due to the lack of pixel level structural matching data, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images. Therefore, this paper proposes a unsupervised learning method based on CycleGAN. On the basis of CycleGAN, a pre trained edge monitor is introduced to calculate the edge feature map before and after image transformation, and the edge similarity loss function is calculated as the basis for optimizing the neural network parameters. The experimental results show that the proposed method effectively reduces the loss of effective edge information during the coloring process and suppresses the generation of abnormal edge information during the coloring process.
Infrared detectors have a wide range of applications in temperature monitoring, industrial detection, automotive auxiliary driving, and material identification due to their unique capabilities such as temperature sensitivity, molecular bond vibration recognition, and haze penetration. Infrared detection relies on infrared materials such as InGaAs, InSb, MCT and superlattices to show good photoelectric properties. Unfortunately, these traditional infrared detection materials require high-quality lattice-matched single crystal substrates to grow on and electronically interconnect with the readout circuit by inverted bonding, which greatly limits their application due to their high cost and complexity. In recent years, new infrared materials such as colloidal quantum dots, black phosphorus, MoS2 and graphene have shown excellent infrared detection properties. Among many new infrared materials, HgTe colloidal quantum dots have the widest tunable absorption wavelength, including short-wave infrared (1.5~ 2.5 μm), mid-wave infrared (3~5 μm), and long-wave infrared (8~12 μm). Moreover, HgTe colloidal quantum dots have a solution process that is low cost and can be used for mass integration. For HgTe colloidal quantum dot infrared detector, we have carried out a series of research and exploration from single-element devices to focal plane array. In this paper, we summarize our recent progress in HgTe colloidal quantum dots infrared detectors from single-element devices to focal plane arrays.
Infrared multispectral imaging with focal plane array (FPA) is attracting great interest with increasing demand for sensitive, low-cost and scalable devices that can distinguish coincident spectral information. However, the widespread use of such detectors is still limited by the complex material growth process, low energy band tunability, and high defect density in epitaxial semiconductors like HgCdTe, InSb, and InGaAs, which hinders the development of dual-band detectors. In contrast, the solution-processability and wide spectral tunability of colloidal quantum dots (CQDs) have inspired various inexpensive, high-performance optoelectronic devices. Here, we demonstrate a two-terminal CQDs dual-band detector, which provides bias-switchable spectral response in two distinct bands. A vertical stack of two rectifying junctions in “back-to-back” diodes configuration is created by engineering a strong and spatially stable doping process. By controlling the bias polarity and magnitude, the detector can be rapidly switched between short-wave infrared (SWIR) and mid-wave infrared (MWIR) at modulation frequencies up to 100 kHz with D* above 1010 Jones at cryogenic temperature. The emergence of colloidal quantum dots demonstrated some potential routes leading to dual-band infrared imaging FPA.
Conference Committee Involvement (3)
Infrared Technology and Applications
23 July 2024 | Beijing, China
Terahertz Technology and Applications
25 July 2023 | Beijing, China
2021 International Conference on Optical Instruments and Technology: IRMMW-THz Technologies and Applications
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