Generative adversarial networks are being extensively studied for low-dose computed tomography denoising. However, due to the similar distribution of noise, artifacts, and high-frequency components of useful tissue images, it is difficult for existing generative adversarial network-based denoising networks to effectively separate the artifacts and noise in the low-dose computed tomography images. In addition, aggressive denoising may damage the edge and structural information of the computed tomography image and make the denoised image too smooth. To solve these problems, we propose a novel denoising network called artifact and detail attention generative adversarial network. First, a multi-channel generator is proposed. Based on the main feature extraction channel, an artifacts and noise attention channel and an edge feature attention channel are added to improve the denoising network’s ability to pay attention to the noise and artifacts features and edge features of the image. Additionally, a new structure called multi-scale Res2Net discriminator is proposed, and the receptive field in the module is expanded by extracting the multi-scale features in the same scale of the image to improve the discriminative ability of discriminator. The loss functions are specially designed for each sub-channel of the denoising network corresponding to its function. Through the cooperation of multiple loss functions, the convergence speed, stability, and denoising effect of the network are accelerated, improved, and guaranteed, respectively. Experimental results show that the proposed denoising network can preserve the important information of the low-dose computed tomography image and achieve better denoising effect when compared to the state-of-the-art algorithms.

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN .

Convolutional neural network (CNN)-based deep learning techniques have enjoyed many successful applications in the field of medical imaging. However, the complicated between-manifold projection from the projection domain to the spatial domain hinders the direct application of CNN techniques in computed tomography (CT) reconstruction. In this work, we proposed a novel CT reconstruction framework based on a CNN, i.e., an intelligent back-projection network (iBP-Net). The proposed iBP-Net method fused a pre-CNN, a back-projection layer, and a post-CNN into an end-to-end network. The pre-CNN adopted CNN techniques to model a filtering operation in the projection domain. In the back-projection layer, a back-projection operation was employed to perform between-manifold projection. Based on CNN techniques, the post-CNN worked together with the pre-CNN to recover reconstructed images from the outputs of the back-projection layer in the spatial domain while maintaining high visual sensitivity. The experimental results demonstrate the feasibility of the proposed iBP-Net framework for CT reconstruction.

Manufacturing process and human operational errors may cause small-sized defects, such as cracks, over-welding, and black edges, on solar cell surfaces. These surface defects are subtle and, therefore, difficult to observe and detect. Accurate detection and replacement of defective battery modules is necessary to ensure the energy conversion efficiency of solar cells. To improve the adaptability to the scale changes of various types of surface defects of solar cells, this study proposed a multi-feature region proposal fusion network (MF-RPN) structure to detect surface defects. In such a network, region proposals are extracted from different feature layers of convolutional neural networks. Additionally, considering that multiple aspect ratios and scale settings and the use of multiple RPNs, result in an overlap of candidate regions and lead to information redundancy, we designed a multiscale region proposal selection strategy (MRPSS) to reduce the number of region proposals and improve network accuracy. Owing to the complete learning of shallow-detail texture information and deep semantic information, our multiscale RPN fusion structure can effectively improve an object’s multiscale feature extraction ability for various scales and types of surface defects of solar cells. Experimental results demonstrate that our method outperforms other methods by achieving a higher detection accuracy.

Given the escalating potential risk associated with X-ray radiation exposure to patients, scholars have been dedicated to investigating advanced algorithms for low-dose CT (LDCT) imaging. The utilization of Generative Adversarial Networks (GANs) has gained significant traction in the field of LDCT denoising research over the past few years, owing to its notable performance advantages. However, variations in the distribution intensity of noise and artifacts in the LDCT image can compromise the robustness and denoising performance of GAN. In this paper, we propose a scale-sensitive generative adversarial network to enhance the robustness of GAN used in LDCT denoising. Our proposed network incorporates an error feedback pyramid generator to improve the denoising network’s sensitivity towards noise and artifacts present in LDCT images by performing feature extraction and noise reduction at different scales. Additionally, we introduce a multi-scale residual discriminator with a wider receptive field and stronger feature extraction ability to enhance the discrimination between true and false samples. Furthermore, specific constraints are incorporated into the loss functions based on the network structure, thereby enhancing the effect of adversarial training between the error feedback pyramid generator and multi-scale residual discriminator while improving the performance of the denoising network. Experimental results demonstrate that our proposed network outperforms state-of-the-art methods in terms of artifact suppression, structural preservation, and model generalization capability for LDCT denoising.

The existing pedestrian detection algorithms are not robust in the case of noise, obstruction and illumination change. To solve the problem, we propose a p