Infrared Thermal Image Enhancement in Cold Spot Detection of Condenser Air Ingress

Abstract

The cold spot identification approach is limited due to the lack of high-resolution infrared thermal images. To solve the problem, infrared thermal images are enhanced using several ways. To improve the thermal images for cold spot detection, researchers used CLAHE, the Canny edge detection method, and deep learning approaches based on denoising autoencoder. The comparison of several enhancement methods based on quality metric factors leads to the selection of the best method. The noise in the Infrared (IR) image is reduced by using a high-resolution autoencoder. The ability to convert a 32 × 32 infrared image to a 64 x 64 resolution image is demonstrated. This study presents an information visibility restoration technique that includes stacked Denoising Autoencoder (DAE) to improve anomalous areas in the condenser's infrared thermal images keeping in mind the current popularity of deep learning models in machine learning. The use of a deep learning autoencoder improves structural similarity index of the image, which is comprehensive. The structural similarity index of the image is improved when a deep learning autoencoder is used. In comparison to CLAHE and the Canny edge detection approach, substantial research indicates that the High-resolution autoencoder is best suited for IR image improvement. Thermal imaging, the suggested technique can improve anomalies without sacrificing crucial information when compared to the straight discriminant analysis.