  In computer-aided diagnosis (CAD) focused on microscopy, denoising improves
the quality of image analysis. In general, the accuracy of this process may
depend both on the experience of the microscopist and on the equipment
sensitivity and specificity. A medical image could be corrupted by both
intrinsic noise, due to the device limitations, and, by extrinsic signal
perturbations during image acquisition. Nowadays, CAD deep learning
applications pre-process images with image denoising models to reinforce
learning and prediction. In this work, an innovative and lightweight deep
multiscale convolutional encoder-decoder neural network is proposed.
Specifically, the encoder uses deterministic mapping to map features into a
hidden representation. Then, the latent representation is rebuilt to generate
the reconstructed denoised image. Residual learning strategies are used to
improve and accelerate the training process using skip connections in bridging
across convolutional and deconvolutional layers. The proposed model reaches on
average 38.38 of PSNR and 0.98 of SSIM on a test set of 57458 images overcoming
state-of-the-art models in the same application domain
