Microscopic image super resolution using deep convolutional neural networks

Recently, deep convolutional neural networks (CNNs) have achieved excellent results in single image super resolution (SISR). Owing to the strength of deep CNNs, it gives promising results compared to state-of-the-art learning based models on natural images. Therefore, deep CNNs techniques have also been successfully applied to medical images to obtain better quality images. In this study, we present the first multi-scale deep CNNs capable of SISR for low resolution (LR) microscopic images. To achieve the difficulty of training deep CNNs, residual learning scheme is adopted where the residuals are explicitly supervised by the difference between the high resolution (HR) and the LR images and HR image is reconstructed by adding the lost details into the LR image. Furthermore, gradient clipping is used to avoid gradient explosions with high learning rates. Unlike the deep CNNs based SISR on natural images where the corresponding LR images are obtained by blurring and subsampling HR images, the proposed deep CNNs approach is tested using thin smear blood samples that are imaged at lower objective lenses and the performance is compared with the HR images taken at higher objective lenses. Extensive evaluations show that the superior performance on SISR for microscopic images is obtained using the proposed approach.