An Enhanced Model for Mammogram Image Denoising Using a CNN Autoencoder with Residual Connections

Abstract

Image denoising has a wide range of applications in obtaining better-quality noisy data, such as medical imaging and low-light photography. This work proposes a new CNN autoencoder architecture also equipped with a residual connection that advances state-of-the-art performance on this denoising benchmark. The proposed model is compared with state-of-the-art methods, including conditional GAN, CNNs with advanced loss functions, and self-supervised learning models in terms of PSNR, SSIM, and computational loss. It achieves the best results on metrics of PSNR at 44.82, SSIM at 0.970, and a loss value as low as 0.015 on a custom dataset of medical images. These results reveal the efficiency of the architecture in noise reduction while retaining the critical information about the structure and, hence, have many promises in real-world applications. The emphasis here is on an application of residual connections fused with deep learning models having superior state-of-the-art performance in image denoising.