We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at.Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.In this work, we propose a novel light field fusion network-LFNet, a CNNs-based light field saliency model using 4D light field data containing abundant spatial and contextual information. selleck compound The proposed method can reliably locate and identify salient objects even in a complex scene. Our LFNet contains a light field refinement module (LFRM) and a light field integration module (LFIM) which can fully refine and integrate focusness, depths and objectness cues from light field image. The LFRM learns the light field residual between light field and RGB images for refining features with useful light field cues, and then the LFIM weights each refined light field feature and learns spatial correlation between them to predict saliency maps. Our method can take full advantage of light field information and achieve excellent performance especially in complex scenes, e.g., similar foreground and background, multiple or transparent objects and low-contrast environment. Experiments show our method outperforms the state-of-the-art 2D, 3D and 4D methods across three light field datasets.In this work we present SpliNet, a novel CNNbased method that estimates a global color transform for the enhancement of raw images. The method is designed to improve the perceived quality of the images by reproducing the ability of an expert in the field of photo editing. The transformation applied to the input image is found by a convolutional neural network specifically trained for this purpose. More precisely, the network takes as input a raw image and produces as output one set of control points for each of the three color channels. Then, the control points are interpolated with natural cubic splines and the resulting functions are globally applied to the values of the input pixels to produce the output image. Experimental results compare favorably against recent methods in the state of the art on the MIT-Adobe FiveK dataset. Furthermore, we also propose an extension of the SpliNet in which a single neural network is used to model the style of multiple reference retouchers by embedding them into a user space. The style of new users can be reproduced without retraining the network, after a quick modeling stage in which they are positioned in the user space on the basis of their preferences on a very small set of retouched images.The shear mode film bulk acoustic wave resonators (FBARs) have been developed rapidly in recent years due to the increased interest in biosensors. In this paper, the mode coupling effects which are unavoidable in real devices are theoretically analyzed and discussed. Mindlin’s approximation method of expanding the displacement fields with power series along the plate thickness direction is employed and a system of two-dimensional plate equations is obtained. The accuracy of the plate theory is validated through the comparison of dispersion curves with the results calculated by three-dimensional exact theory. Free coupling vibrations of the cross-sectional plane of finite-sized shear mode FBAR plates are further studied with the derived two-dimensional theory. Results in this paper show that the presented plate theory is accurate and efficient for the structural analyses of shear mode FBARs. Besides, the selection of aspect ratios can be used to avoid strong mode couplings, which is helpful for device designs.This paper provides a fundamental study into the trade-offs between the location of piezoceramic elements, resonant frequency, and achievable ultrasonic vibration amplitude at the working end of the Bolted Langevin-style Transducers (BLT) for Ultrasonically Assisted Machining (UAM) applications. Analytical models and Finite Element (FE) models are established for theoretical study, which are then validated by experiments on four real electro-mechanical transducers. Results suggest that resonant frequency and oscillation amplitude of the BLTs depend essentially on the dimensions of the system and the location of piezoceramic elements. The highest resonant frequency and the maximal vibration are achieved when the piezoceramic elements are at the longitudinal displacement node, where the highest effective electro-mechanical coupling coefficient value is exhibited. However, the minimal resonant frequency and the lowest vibration, which is almost equal to zero, are observed when the piezoceramic elements are located at the displacement anti-node.