Search

Blur is a key property in the perception of COVID‐19 computed tomography (CT) image manifestations. Typically, blur causes edge extension, which brings shape changes in infection regions. Tchebichef moments (TM) have been verified efficiently in shape representation. Intuitively, disease progression of same patient over time during the treatment is represented as different blur degrees of infection regions, since different blur degrees cause the magnitudes change of TM on infection regions image, blur of infection regions can be captured by TM. With the above observation, a longitudinal objective quantitative evaluation method for COVID‐19 disease progression based on TM is proposed. COVID‐19 disease progression CT image database (COVID‐19 DPID) is built to employ radiologist subjective ratings and manual contouring, which can test and compare disease progression on the CT images acquired from the same patient over time. Then the images are preprocessed, including lung automatic segmentation, longitudinal registration, slice fusion, and a fused slice image with region of interest (ROI) is obtained. Next, the gradient of a fused ROI image is calculated to represent the shape. The gradient image of fused ROI is separated into same size blocks, a block energy is calculated as quadratic sum of non‐direct current moment values. Finally, the objective assessment score is obtained by TM energy‐normalized applying block variances. We have conducted experiment on COVID‐19 DPID and the experiment results indicate that our proposed metric supplies a satisfactory correlation with subjective evaluation scores, demonstrating effectiveness in the quantitative evaluation for COVID‐19 disease progression.

In haze weather, the image contrast and clarity are often poor, which affects the visual effect of the image. Therefore, it is of great significance to study a fast and effective method to clear the haze image. This paper proposes an image dehazing framework based on gradient distribution prior. Firstly, the method is to obtain a prior model of the gradient distribution of images from a large number of high-quality natural image data sets. Secondly, the gradient distribution of the hazy image is changed to make the infinite approximation of the learned prior model. Finally, the Poisson equation is used to solve the reconstructed image to obtain dehazing image. Experimental results show that the proposed method can remove the haze of image effectively. Compared with the most advanced methods, the image processed by this algorithm not only retains more details of the original image, but also improves the image definition largely.

Recent years have witnessed the explosive growth of multimedia applications over networks and increasingly high requirements of consumers for multimedia signals in terms of quality of experience (QoE). Effective and efficient yet energy-saving saliency detection model and quality prediction method are eagerly desired, since they play critical roles in raising users' QoE and promoting the progress of green multimedia communication. Current studies of saliency detection and quality evaluation are far from ideal yet. In this paper we investigate the influence of complexity on visual saliency and quality. Complexity is an essential concept in human perception to visual stimulus, but it is substantially abstract and hard to be endowed with a clear definition. We suppose that brain systematically combines global and local features during the whole process of human perception. Global features lead a dominant position in seeking salient areas under the condition that image complexity is high, namely without obviously isolated foreground objects, whereas local features play a key role in an opposite situation. With this consideration, this paper establishes a novel framework for detecting visual saliency based on image complexity estimation before complexity-adaptive merging of global and local features. Furthermore, the concept of complexity is deployed for blind photographic image quality assessment (IQA) by means of saliency-based weighting. Features which refer to contrast, artifacts, brightness and natural scene statistics (NSS) are modified and integrated to derive a blind IQA model and predict the quality of photos. Based on the above two technologies, this paper introduces smart phones as mobile terminals, cloud platforms for speed-up and energy-saving, and wireless networks for transmission, and provides a practical mobile multimedia application. Comparative experiments validate that, within this application system, our proposed saliency detection model and blind photographic IQA method implement better than existing relevant competitors in terms of effectiveness and efficiency comparison.

Multimodal medical image fusion (MMIF) plays critical roles in image-guided clinical diagnostics and treatment. Pulse coupled neural network (PCNN) has been applied in image fusion for several years. In the schemes of image fusion based on PCNN, the authors have adjusted variables manually, so that it is difficult to get satisfying effects which limit in dealing with medical images with different modalities. This paper presents a quality-guided adaptive optimization method for MMIF, which is based on PCNN optimized by multi-swarm fruit fly optimization algorithm (MFOA). To reduce the implementation cost and improve the performance of the MFOA, quality assessment for multimodal medical image fusion was chosen to be the hybrid fitness function. Guided by such quality measurement, the adaptive PCNN using the MFOA (PCNN-MFOA) is proposed, which could automatically fit the optimal variables to the source images and enhance the fusion effect. The experimental results visually and quantitatively show that the proposed fusion strategy is more effective than the state-of-the-art methods and it is more effective in processing medical images with different modalities.

Linear Wireless Sensor Networks (LWSN) play an important role in bridge healthy monitoring, such as vibration, deformation, stress and so on. It is basically based on the observation that energy balance and delivery rate directly determine the running time of the networks. So, we proposed an energy balance aggregate model. Specially, we first compute the energy consumption and data delivery of each node in the network according to LWSN network model. The calculation conditions of energy balancing and delivery rate are then obtained. Finally, we have tested the performance of the proposed model. Simulation experimental results demonstrate that the proposed method can produce reliable performance, which is suitable for bridge healthy monitoring.

In this paper, an improved model based on the combination of residual and inverted residual blocks is proposed for image expression recognition, named as bi-directional residual network. The main objective of the proposed method is to alleviate the problem of feature dispersion due to the deep network level in traditional expression recognition research. In this case, residual block is a good solution. However, residual network with small scale of training data can easily lead to over-fitting, which is often the case for image expression recognition. To improve the robustness of the network during training, inverted residual blocks are therefore adopted. Depending on the organization sequence of residual blocks and inverted residual blocks, three network structures are proposed and studied. Fer2013 and CK+ datasets in facial field are adopted for experiment. The experimental results show that the optimized algorithm improves the accuracy by 2.79% on Fer2013 dataset compared with ResNet-50 models.

An improved image segmentation model was established to achieve accurate detection of target contours under high noise, low resolution, and uneven illumination environments. The new model is based on the variational level set algorithm, which improves the C-V (Chan and Vese) model, fuses the contour and area models to segment the image information, and solves the problem of optimal solution of the energy model by finding the steady-state solution of the partial differential equation. It can improve the calculation accuracy, topological structure adaptability, anti-noise ability, and reduce the light sensitivity effectively. Experiment shows that the new model has good robustness, high real-time performance, and it can effectively improve detection accuracy.

This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

A compact three-layer circuit structure based on the double-sided parallel-strip lines (DSPSLs) is proposed in this article to design an out-of-phase power divider (PD) with miniaturized isolation ...