LD-Net: An Efficient Lightweight Denoising Model Based on Convolutional Neural Network

LD-Net: An Efficient Lightweight Denoising Model Based on Convolutional Neural Network

Blog Article

The removal of impulse noise is a crucial pre-processing step in image processing systems.In recent years, numerous noise-removal methods have been proposed to improve denoizing performance and reconstruct noise-free images.However, removing Radio high-density impulse noise remains a major challenge.In this paper, to address the image denoizing problem associated with high-density noise, we propose a new denoizing model, called LD-Net, which can be trained end-to-end and directly reconstructs noise-free images via a lightweight convolutional neural network.LD-Net is performed in two stages including a feature augmentation stage and a feature refinement stage.

During the feature augmentation stage, the spatial size and dimension of the input image are increased by employing the deconvolutional layers for effective feature learning.During the Ball - Glove Baseball Fielding - Senior feature refinement stage, the textural details of the image are enhanced for the reconstruction of the noise-free image by the utilization of a proposed sequence of three convolutional layers.Quantitative and qualitative evaluations performed on the SN-LABELME dataset indicate that the proposed LD-Net removes high-density impulse noise more effectively and at higher speed than other state-of-the-art denoizing methods.