Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.

Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.

Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN.

Graph convolutional network (GCN) has attracted increasing attention in hyperspectral image (HSI) classification due to its capability to capture the long-range correlations between adjacent land covers. Most GCN-based HSI classification methods have been proposed to address the four limitations (shape-fixed kernel, massive calculations and parameters, limited classification ability with limited labelled samples, and difficult to capture the long-term relationships of land covers) of Convolutional Neural Networks (CNNs) by operating on superpixel-based nodes. However, the pixels in each superpixel share the spectral-spatial features, overlooking the unique characteristics of individual pixels. To address these limitations of GCN and CNN and fully exploit their advantages, we propose a novel Multi-Branch Attention Framework (MFAF), in which the specially designed GCN and Convolutional Neural Network (CNN) branches learn the complementary spectral-spatial features. Specifically, we develop a multi-scale attentional graph convolutional network to enhance the ability to understand the long-range correlations between land covers, accomplished by constructing the multi-scale attentional adjacency matrix. Then, based on the two designs of the dual branch depthwise separable convolution and the attention-based residual block, we present a new complementary dual convolutional attention network that extracts more discriminative spectral-spatial features of pixels. Finally, we introduce an Attention Fusion Pooling (AFP) mechanism to combine the features generated by different network branches. Extensive experimental evaluations on four public HSI datasets demonstrate that the proposed MFAF achieves better performance than several state-of-the-art methods, delivering superior and consistent results in terms of overall accuracy (OA), average accuracy (AA), and kappa coefficient (KAPPA).