Image deblurring attracts research attention in the field of image processing and computer vision. Traditional methods based on statistical prior largely depend selected type, which limits their restoring ability. Moreover, constructed model is difficult to solve, operation comparatively complicated. Meanwhile, deep learning has become a hotspot various fields recent years. End-to-end convoluti...

This paper focuses on establishing $$L^2$$ approximation properties for deep ReLU convolutional neural networks (CNNs) in two-dimensional space. The analysis is based a decomposition theorem kernels with large spatial size and multi-channels. Given the result, property of activation function, specific structure channels, universal CNNs classic obtained by showing its connection one-hidden-layer...

Human action is a high-level concept in computer vision research and understanding it may benefit from different semantics, such as human pose, interacting objects, and scene context. In this paper, we explicitly exploit semantic cues with aid of existing human/object detectors for action recognition in videos, and thoroughly study their effect on the recognition performance for different types...

In this paper, we propose a novel convolution neural networks (CNNs) based method for nodule type classification. Compared with classical approaches that are handling four solid nodule types, i.e., well-circumscribed, vascularized, juxtapleural and pleural-tail, our method could also achieve competitive classification rates on ground glass optical (GGO) nodules and non-nodules in computed tomog...

Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. CNNs require billions of operations to process a frame. This computational complexity, combined with the inherent parallelism of the convolution operation make CNNs an excellent target for custom accelerator...

Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer of the graph or key node neighborhoods of the graph into the same fixed form. Du...

Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. ...