Image Inpainting Based on Wavelet Decomposition

AbstractImage inpainting is an important problem image processing. It is a difficult problem to simultaneously fill-in the texture and structure in regions of missing image information. In order to inpaint the damaged image with both missing the structure and texture information, an image inpainting algorithm based on wavelet decomposition is presented. First the damaged image is decomposed into structure sub-image and texture sub-image using the wavelet transformation. Then, the sub-image with the region of missing information in the structure is reconstructed by Curvature-Driven Diffusions (CDD) algorithm, while the same region in the texture sub-image is filled-in with the improved texture synthesis based on exemplar; Finally, the restored image is given by recombining the structure and texture restored results. A large number of experiments show that the proposed algorithm can quickly and efficiently restore the structure and texture information at the same time, and the visual effects and the Peak Signal to Noise Ratio (PSNR) is better than the similar algorithms

Na+ Permeation and Block of hERG Potassium Channels

The inactivation gating of hERG channels is important for the channel function and drug–channel interaction. Whereas hERG channels are highly selective for K+, we have found that inactivated hERG channels allow Na+ to permeate in the absence of K+. This provides a new way to directly monitor and investigate hERG inactivation. By using whole cell patch clamp method with an internal solution containing 135 mM Na+ and an external solution containing 135 mM NMG+, we recorded a robust Na+ current through hERG channels expressed in HEK 293 cells. Kinetic analyses of the hERG Na+ and K+ currents indicate that the channel experiences at least two states during the inactivation process, an initial fast, less stable state followed by a slow, more stable state. The Na+ current reflects Na+ ions permeating through the fast inactivated state but not through the slow inactivated state or open state. Thus the hERG Na+ current displayed a slow inactivation as the channels travel from the less stable, fast inactivated state into the more stable, slow inactivated state. Removal of fast inactivation by the S631A mutation abolished the Na+ current. Moreover, acceleration of fast inactivation by mutations T623A, F627Y, and S641A did not affect the hERG Na+ current, but greatly diminished the hERG K+ current. We also found that external Na+ potently blocked the hERG outward Na+ current with an IC50 of 3.5 mM. Mutations in the channel pore and S6 regions, such as S624A, F627Y, and S641A, abolished the inhibitory effects of external Na+ on the hERG Na+ current. Na+ permeation and blockade of hERG channels provide novel ways to extend our understanding of the hERG gating mechanisms

Automatic depression scale prediction using facial expression dynamics and regression

Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behaviour, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.The work by Asim Jan was supported by School of Engineering & Design/Thomas Gerald Gray PGR Scholarship. The work by Hongying Meng and Saeed Turabzadeh was partially funded by the award of the Brunel Research Initiative and Enterprise Fund (BRIEF). The work by Yona Falinie Binti Abd Gaus was supported by Majlis Amanah Rakyat (MARA) Scholarship

Electromechanical-Mode Coupling Model and Failure Prediction of CFRP under Three-Point Bending

Carbon fiber reinforced polymer materials (CFRP) cause CFRP to bend or fail when subjected to external loads or impacts. In the case of static three-point bending, using the conductive properties of the carbon fiber inside the CFRP, the overall damage detection and failure prediction can be carried out by electromagnetic methods. The eddy current coil is used to realize real-time monitoring of damage, and the measured voltage value can be mapped to obtain the load of the sample. This paper conducts theoretical analysis and experimental verification, and obtains the relationship between CFRP stress damage and spatial conductivity change, and proposes a CFRP electromechanical coupling model under quasistatic three-point bending. Combined with the theory of electrically ineffective length, the CFRP three-point bending electromechanical coupling model was revised. Experimental results prove that the revised model can describe the load-conductivity change trend of three-dimensional braided CFRP more accurately, which provides a theoretical basis for monitoring the structural health of CFRP through electromagnetic methods