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RDAGAN:A Residual Dense Attention GAN for Microscopic Image Super-Resolution
Version 1
: Received: 7 May 2024 / Approved: 8 May 2024 / Online: 8 May 2024 (08:19:25 CEST)
A peer-reviewed article of this Preprint also exists.
Liu, S.; Weng, X.; Gao, X.; Xu, X.; Zhou, L. A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution. Sensors 2024, 24, 3560. Liu, S.; Weng, X.; Gao, X.; Xu, X.; Zhou, L. A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution. Sensors 2024, 24, 3560.
Abstract
With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.
Keywords
single image super-resolution; microscopic image; generative adversarial network; image processing
Subject
Engineering, Telecommunications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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