The quality evaluation is an indispensable link in light field (LF) image processing. Most of existing LF objective evaluation indexes do not make effective use of the angular characteristic of LF, so the evaluation results are unsatisfactory. In this letter, the quality evaluation of LF image is constructed based on human visual system (HVS) and LF angular-spatial characteristics. Based on the fact that HVS has different sensitivity to different parallaxes, we assume that LF image quality perceived by human eyes has the optimal parallax range. A dual-fan filter is used to constrain the parallax range. Then, the overall quality of the LF is represented by combining the spatial and angular quality, which performed from the central sub-aperture image and the focus stack, respectively. In addition, because the difference of Gaussian (DoG) operator can simulate the process of extracting texture structure by human eyes. The structural similarity of DoG texture feature is utilized in the spatial quality evaluation. Extensive comparison experiments show that the proposed method is more consistent with the characteristics of LF.

The large amount of complex scene information recorded by light field imaging has the potential for immersive media applications. Compression and reconstruction algorithms are crucial for the transmission, storage, and display of such massive data. Most of the existing quality evaluation indexes do not effectively account for light field characteristics. To accurately evaluate the distortions caused by compression and reconstruction algorithms, it is necessary to construct an image evaluation index that reflects the angular-spatial characteristics of the light field. This work proposes a full-reference light field image quality evaluation index that attempts to extract less information from the focus stack to accurately evaluate the entire light field quality. The proposed framework includes three specific steps. First, we construct a key refocused image extraction framework by the maximal spatial information contrast and the minimal angular information variation. Specifically, the gradient and phase congruency operators are used in the extraction framework. Second, a novel light field quality evaluation index is built based on the angular-spatial characteristics of the key refocused images. In detail, the features used in the key refocused image extraction framework and the chrominance feature are combined to construct the union feature. Third, the similarity of the union feature is pooled by the relevant visual saliency map to obtain the predicted score. Finally, the overall quality of the light field is measured by applying the proposed index to the key refocused images. The high efficiency and precision of the proposed method are shown by extensive comparison experiments.

Aspect-based Sentiment Analysis (ABSA), also known as fine-grained sentiment analysis, aims to predict the sentiment polarity of specific aspect words in the sentence. Some studies have explored the semantic correlation between words in sentences through attention-based methods. Other studies have learned syntactic knowledge by using graph convolution networks to introduce dependency relations. These methods have achieved satisfactory results in the ABSA tasks. However, due to the complexity of language, effectively capturing semantic and syntactic knowledge remains a challenging research question. Therefore, we propose an Adaptive Dual Graph Convolution Fusion Network (AD-GCFN) for aspect-based sentiment analysis. This model uses two graph convolution networks: one for the semantic layer to learn semantic correlations by an attention mechanism, and the other for the syntactic layer to learn syntactic structure by dependency parsing. To reduce the noise caused by the attention mechanism, we designed a module that dynamically updates the graph structure information for adaptively aggregating node information. To effectively fuse semantic and syntactic information, we propose a cross-fusion module that uses the double random similarity matrix to obtain the syntactic features in the semantic space and the semantic features in the syntactic space, respectively. Additionally, we employ two regularizers to further improve the ability to capture semantic correlations. The orthogonal regularizer encourages the semantic layer to learn word semantics without overlap, while the differential regularizer encourages the semantic and syntactic layers to learn different parts. Finally, the experimental results on three benchmark datasets show that the AD-GCFN model is superior to the contrast models in terms of accuracy and macro-F1.

Light field (LF) cameras can capture the intensity and angle information of any scene in one single shot, and are widely used in virtual reality, refocusing, de-occlusion, depth estimation, etc. However, the fundamental limitation between angular and spatial resolution leads to low spatial resolution of LF images, which limits their development prospects in various fields. In this paper, we propose a new super-resolution network based on view interaction and hierarchical feature fusion to effectively improve LF image spatial resolution and preserve the consistency of the reconstructed LF structure. Initially, we provide novel view interaction blocks to represent the relationship between all views, and efficiently combine hierarchical features using a feature fusion block consisting of residual dense blocks (RDBs) to more effectively preserve the parallax structure. In addition, in the process of extracting features, we introduce residual channel-reconstruction blocks (RCBs) to minimize the duplication of channels in the features. An inter-view unit (InterU) and an intra-view unit (IntraU) are employed to minimize redundancy in the spatial domain as well. Our suggested method is tested using public LF datasets, which include large-disparity LF images. The experimental results demonstrate that our method exhibits the best performance in terms of both quantitative and qualitative results.