A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution

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 the 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 1.5 dB and 0.2, respectively.

REMA: A Rich Elastic Mixed Attention Module for Single Image Super-Resolution

“…However, one of the major challenges of deep learning-based algorithms is high-frequency detail preservation. Numerous studies have proposed diverse algorithms to address this challenge, including residual learning [4,5], recursive structures [6][7][8], dense connections [9][10][11], and multi-path learning [12,13]. In recent times, attention-based algorithms have gained prominence, notably after the popularity of Transformer-based algorithms.…”