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 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.