Optical Videoscope Image Super-Resolution Based on Convolutional Neural Networks

Image super-resolution is the process performed to improve the resolution of the images from Low Resolution (LR) to High Resolution (HR). Videoscope images are examples of industrial images that have LR. These videoscope images are enhanced in this paper using wavelet multi-scale Convolutional Neura...

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Bibliographic Details
Published inJournal of optics (New Delhi) Vol. 53; no. 3; pp. 2307 - 2315
Main Authors Aboshosha, Sahar, El-Shafai, Walid, El-Banby, Ghada M., Khalaf, Ashraf A. M., El-Rabaie, El-Sayed M., El-Samie, Fathi E. Abd, El-Hag, Noha A.
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.07.2024
Subjects
Lasers
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Research Article
CNN
Videoscope images
Super resolution
Wavelet decomposition
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Summary:Image super-resolution is the process performed to improve the resolution of the images from Low Resolution (LR) to High Resolution (HR). Videoscope images are examples of industrial images that have LR. These videoscope images are enhanced in this paper using wavelet multi-scale Convolutional Neural Networks (CNNs). In this paper, we develop a videoscope super-resolution reconstruction technique based on CNNs and wavelet decomposition. The wavelet decomposition is performed on videoscope images for multi-scale representation. The CNN is trained multiple times to approximate the wavelet multi-scale representations, separately. Thus, multiple CNNs are trained to extract the features of videoscope images in several directions and multi-scale frequency bands, and thus, the HR images can be restored.
ISSN:0972-8821
0974-6900
DOI:10.1007/s12596-023-01344-1