Duong Minh-Thien, Nguyen Thi Bao-Tran, Lee Seongsoo, Hong Min-Cheol
Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Department of Intelligent Semiconductor, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel). 2024 Jun 3;24(11):3608. doi: 10.3390/s24113608.
Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.
图像去噪在从成像传感器中去除加性噪声的计算机视觉任务中被视为一个不适定问题。最近，几种基于卷积神经网络的图像去噪方法取得了显著进展。然而，由于图像内容的复杂性，简单的去噪网络很难恢复出美观的图像。因此，本研究提出了一种多分支网络来提高去噪方法的性能。首先，所提出的网络基于传统的自动编码器进行设计，以从输入图像中学习多级上下文特征。随后，我们将两个模块集成到网络中，包括金字塔上下文模块（PCM）和残差瓶颈注意力模块（RBAM），以在训练过程中提取显著信息。更具体地说，PCM应用于网络开始时，以扩大感受野并使用扩张卷积成功解决全局信息丢失问题。同时，RBAM插入到编码器和解码器中间，以消除退化特征并减少不需要的伪影。最后，大量实验结果证明了所提出的方法在客观和主观性能方面优于当前最先进的深度学习方法。
