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 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 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.

中文翻译：

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LD-Net：基于卷积神经网络的高效轻量化降噪模型

脉冲噪声的去除是图像处理系统中至关重要的预处理步骤。近年来，已经提出了许多消除噪声的方法来改善去噪性能并重建无噪声图像。然而，去除高密度脉冲噪声仍然是主要挑战。在本文中，为了解决与高密度噪声相关的图像去噪问题，我们提出了一种称为LD-Net的新去噪模型，该模型可以端到端地进行训练，并通过轻量级的卷积神经网络直接重建无噪声的图像。网络。LD-Net分两个阶段执行，包括特征增强阶段和特征完善阶段。在特征增强阶段，通过使用反卷积层进行有效的特征学习，可以增加输入图像的空间大小和尺寸。在特征细化阶段，通过利用三个卷积层的建议序列，可以增强图像的纹理细节，以重建无噪声图像。在SN-LABELME数据集上进行的定量和定性评估表明，与其他最新的去噪方法相比，拟议的LD-Net可以更有效，更快速地消除高密度脉冲噪声。