Computed Tomography (CT) imaging has become a commonly used technique in healthcare to identify irregularities in the human body. However, CT scans involve exposure to electromagnetic radiation, which can pose health risks to patients. To address this, Low-Dose CT has been introduced, but it results in degraded image quality, including increased noise, artifacts, and loss of edge and feature contrast. This can limit the effectiveness of Computer-Aided Diagnosis systems. Denoising and preserving edge sharpness in Low-Dose CT images is a challenging task that conventional denoising techniques may not efficiently solve. Deep learning-based methods have emerged as a potential solution to this problem. This study proposes a new unsupervised LDCT image denoising algorithm called DEPnet (Denoise and Edge Preserve), which utilises a U-Net-based autoencoder with hybrid dilated convolution and batch normalization layers. The proposed algorithm has been evaluated on the KiTS19 Low-Dose CT Grand Challenge dataset and compared with other models such as Q-AE, Msaru-Net, and CT-ReCNN. The results demonstrate that DEPnet effectively reduces noise in LDCT images and preserves fine details, making it a promising solution for denoising Low-Dose CT images. © 2023 IEEE.