Citation: Wang, Y.; Zhang, J.;
Wang, L. Compressed Sensing
Super-Resolution Method for
Improving the Accuracy of Infrared
Diagnosis of Power Equipment. Appl.
Sci. 2022, 12, 4046. https://doi.org/
10.3390/app12084046
Academic Editors: Pierluigi Siano,
Hassan Haes Alhelou and
Amer Al-Hinai
Received: 3 March 2022
Accepted: 12 April 2022
Published: 16 April 2022
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Article
Compressed Sensing Super-Resolution Method for Improving
the Accuracy of Infrared Diagnosis of Power Equipment
Yan Wang, Jialin Zhang and Lingjie Wang *
School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China;
wang_yan0421@163.com (Y.W.); sas_0@ncepu.edu.cn (J.Z.)
* Correspondence: 2192213093@ncepu.edu.cn
Abstract:
The infrared image of power equipment plays a crucial role in identifying faults, monitoring
equipment condition, and so on. The low resolution and low definition of infrared images in
applications contribute to the low accuracy of infrared diagnosis. A super-resolution reconstruction
method of infrared image, based on compressed sensing theory, is proposed. Firstly, by analyzing
the variation of high-frequency information in infrared images with different blurring degrees, the
image gradient norm ratio is introduced to estimate the blur kernel matrix in the degradation model
a priori. Then, in the process of image reconstruction, we add the full variational regularization
term to the traditional compressed sensing model, and design a two-step full variational sparse
reconstruction algorithm. Experimental results verify the effectiveness of the method. Compared
with the existing classical super-resolution methods, this method offers improvement in subjective
visual effect and objective evaluation index. In addition, the final image recognition and infrared
diagnosis experiments show that this method is helpful to improve the accuracy of infrared diagnosis
of power equipment.
Keywords:
compressed sensing; super-resolution; fault diagnosis; infrared image; power equipment
1. Introduction
With the proposal of the concept of the Internet of Things and the continuous pro-
motion of the construction of the smart grid, the use of infrared diagnosis technology for
real-time monitoring and fault diagnosis of power equipment can effectively improve the
operation reliability of the power grid [
1
–
3
]. However, there is a wide range of electrical
equipment in the power grid, and the large-scale deployment of expensive traditional high-
resolution infrared sensors will cause enterprises to incur unbearable costs [
4
,
5
]. Therefore,
how to use low-cost, low-resolution infrared sensors to achieve the effect of high-resolution
infrared sensors is the key to promote the application of infrared diagnosis technology in
the Internet of Things power system [6].
The main purpose of super resolution (SR) imaging technology is to overcome the
limitations of low-cost and low-precision image acquisition devices. Through single- frame
or multi-frame low resolution (LR) image input, it is necessary to complete image recon-
struction according to different prior knowledge, obtain high resolution (HR) images, and
recover high-frequency information lost in the process of image acquisition [
7
]. Accord-
ing to the number of LR images inputted during reconstruction, super-resolution can be
divided into single-frame and multi-frame image super-resolution. Among these, multi-
frame image super-resolution methods include the interpolation method [
8
,
9
], iterative
back projection method [
10
], maximum likelihood estimation method [
11
], sparse coding
method [
12
] and learning-based method [
13
,
14
]. The super-resolution method of multiple
images uses complementary information contained in different images to improve the
resolution by registering multiple images obtained at similar time positions in the same
scene. The single-frame super-resolution method only uses a single image in a scene, which
Appl. Sci. 2022, 12, 4046. https://doi.org/10.3390/app12084046 https://www.mdpi.com/journal/applsci
