Color guided thermal image super resolution

A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution restoration (SRR) technique converting a thermal image acquired by a low-resolution camera into a highresolution one, and a thermal image deblurring method have been researched. However, existing studies were performed based on 1-channel (grayscale) images. In addition, a large-sized and whole image has been used in the existing thermal image deblurring methods, which causes lower deblurring performance. In this study, we propose novel SRR and deblurring methods. The proposed deblurring method is conducted based on small region images. The proposed methods are also conducted using 3-channel (color) thermal images and generative adversarial networks. In addition, the performances of this method are compared in various color spaces (RGB, Gray, HLS, HSV, Lab, Luv, XYZ, YCrCb), image sizes, and thermal databases. Through experiments using selfcollected databases and open databases, it was confirmed that the proposed methods show better performance than the state-ofthe-art methods. INDEX TERMS thermal image; super-resolution reconstruction; deep learning; generative adversarial network; image deblurring.

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“…Ref. [30] proposed a color guided SRR method for thermal images. In this study, SRR was performed using visible-light and thermal images.…”

Section: Related Work a Super-resolution Reconstructionmentioning

confidence: 99%

“…Without deeplearning method 1-channel image [24][25][26][27][28][29][30][31][32] Large-scale data collection, training, and processing are not required.…”

Section: Thermal Imagementioning

confidence: 99%

Thermal Image Reconstruction Using Deep Learning

et al. 2020

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“…The field of image SR is especially attractive. The image super-resolution has many applications, such as medical image processing [7], [8], facial image improvement [9], [10], enhancement of compressed images/videos [11], [12], and thermal image enhancement [13]. Nevertheless, the SR is a broader concept.…”

Section: Introductionmentioning

confidence: 99%

Temporal Graph Super Resolution on Power Distribution Network Measurements

et al. 2021

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The applications of super-resolution (SR) technology in the field of image completion are successful. Nevertheless, industry applications demand not only image completion but also the topology and time-series completion. In this paper, the SR technology on a topology graph is studied in the scenario of recovering measurements in power distribution systems for cost saving and security & stability improvement. The power flow and voltage magnitude measurements on feeders are reported at different frequencies. In this paper, a new data completion method considering distribution system topology is proposed. Firstly, the graph convolutional neural network (GCN) is used for spatial-temporal convolution on a graph, and then the power system state estimation (SE) is used introducing the physical constraints. This method realizes the super-resolution of distribution system measurements, improves the state awareness of distribution systems. Hence, it helps to improve the efficiency of distribution network operation and to reduce equipment failures.

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“…Several experiments can be found also in the literature. Some authors worked on the integration of HR visible sensors and infrared ones, for satellite (Tonooka 2005;Zhang and Huang 2013;Feng et al 2015) and terrestrial (Chen et al 2016;Poblete et al 2018;Turner et al 2014) applications. Bazzo et al (2015) proposed a method that exploits fibre Bragg gratings and distributed optical sensors.…”

Section: Introductionmentioning

confidence: 99%

A multi-image super-resolution algorithm applied to thermal imagery

et al. 2019

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Although thermal imaging is a widely used technique in many applications and is under continuous development, one of its limits is the relatively coarse spatial resolution. Nevertheless, in the last years, a number of super-resolution algorithms have been developed which allow to enhance the resolution of the images. They can be divided in two main different categories: single-image or multiple image-based algorithms. In this work, a multiple image-based algorithm for the super-resolution was implemented, tested and applied to terrestrial thermal imaging with the aim to overcome the limitation of the low resolution. In particular, the method relies on the use of many images acquired from slightly different positions to obtain, thanks to the redundancy of observations, a super-resolution frame having an upsampling factor of four. Several tests were performed on synthetic datasets, and the accuracy of the obtained super-resolution images was investigated. Moreover, an original algorithm capable to identify gross errors during the image registration phase, which is one of the crucial phases, is presented and its reliability assessed. Results showed the effectiveness of the proposed method on both common visible images and thermal infrared ones, since discrepancies between reconstructed and reference values are reduced by 18 and 25% respectively, when compared with a conventional bicubic algorithm. Finally, the proposed method was tested on a case study concerning the thermal survey of the façade of a historical building in Bologna (Palazzo D'Accursio). A dataset of real thermal frames was acquired and a super-resolution image of the subject was generated through the developed algorithm. Strengths and weaknesses of the method were analysed and discussed in the paper.

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Color guided thermal image super resolution

Cited by 33 publications

References 8 publications

Thermal Image Reconstruction Using Deep Learning

Thermal Image Reconstruction Using Deep Learning

Temporal Graph Super Resolution on Power Distribution Network Measurements

A multi-image super-resolution algorithm applied to thermal imagery

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