Abstract
To reduce external disturbances and achieve high vertical resolution, the scanning time for white-light interference microscopy is very short. Because capturing high-resolution (HR) images is time consuming, low-resolution (LR) images are acquired instead. However, HR images are more desirable because they contain more details. To ensure high vertical resolution and high image resolution, one feasible solution is to process the scanned LR images to HR images by single image super-resolution (SISR). In this paper, an interference image super-resolution (IISR) model based on a generative adversarial network (GAN) is proposed. The generator is based on the enhanced super-resolution generative adversarial network (ESRGAN) architecture. With the aim of acquiring more realistic images, the discriminator network is designed using a modified DenseNet architecture, in which the pooling layers are replaced with dilated convolutional layers. The perceptual loss is optimized, and the content loss is upgraded to a continuously differentiable piecewise function. Various microscopy images are tested, including images with and without interference fringes. The IISR model has been proven to restore LR images to HR images. The comparative experiments prove that the proposed model achieves better visual quality than other models, preserving more realistic details.
Highlights
White-light interference microscopy, known as whitelight interferometry (WLI), is a nondestructive, ultraprecise, high-speed and time-saving measurement technique that has revolutionized the field of precision measurement and remains indispensable [1]
Based on enhanced super-resolution generative adversarial network (ESRGAN), we propose an interference image super-resolution (IISR) model to further improve the visual quality of interference microscopy images
These findings prove that the IISR method has a stronger super-resolution capability, fully recovering the details of the high resolution (HR) image
Summary
White-light interference microscopy, known as whitelight interferometry (WLI), is a nondestructive, ultraprecise, high-speed and time-saving measurement technique that has revolutionized the field of precision measurement and remains indispensable [1]. DenseNet has been used as the main network architecture for both generators and discriminators for performing musculoskeletal quality evaluation [27] and medical image super-resolution [26]. It proves that DenseNet offers stronger feature extraction capabilities in GANs. To extract even more abundant features, the residual dense block (RDB) structure has been proposed [28]. A transition layer, which reduces the feature map size through convolution and pooling (Pool), is designed to be placed between each pair of adjacent dense blocks This allows the network to proceed in the ‘‘depth’’ direction.
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