Following the pioneering works of Rudin, Osher and Fatemi on total variation (TV) and of Buades, Coll and Morel on non-local means (NL-means), the last decade has seen a large number of denoising methods mixing these two approaches, starting with the nonlocal total variation (NLTV) model. The present article proposes an analysis of the NLTV model for image denoising as well as a number of improvements, the most important of which being to apply the denoising both in the space domain and in the Fourier domain, in order to exploit the complementarity of the representation of image data. A local version obtained by a regionwise implementation followed by an aggregation process, called Local Spatial-Frequency NLTV (L-SFNLTV) model, is finally proposed as a new reference algorithm for image denoising among the family of approaches mixing TV and NL operators. The experiments show the great performance of L-SFNLTV in terms of image quality and of computational speed, comparing with other recently proposed NLTV-related methods.

Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix A is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics.

Following the pioneering works of Rudin, Osher and Fatemi on total variation (TV) and of Buades, Coll and Morel on non-local means (NL-means), the last decade has seen a large number of denoising methods mixing these two approaches, starting with the nonlocal total variation (NLTV) model. The present article proposes an analysis of the NLTV model for image denoising as well as a number of improvements, the most important of which being to apply the denoising both in the space domain and in the Fourier domain, in order to exploit the complementarity of the representation of image data in both domains. A local version obtained by a regionwise implementation followed by an aggregation process, called Local Spatial-Frequency NLTV (L- SFNLTV) model, is finally proposed as a new reference algorithm for image denoising among the family of approaches mixing TV and NL operators. The experiments show the great performance of L-SFNLTV, both in terms of image quality and of computational speed, comparing with other recently proposed NLTV-related methods.

Gradual change reliability design of mechanical components is considered by semi-stochastic process model of loading with change of time and fully stochastic process model of known degradation function of strength at present, yet it can't obtain reasonable reliability design by existing information of components for these models. Thus the influence of existing information and time-dependent parameters of components on reliability should be taken into account in order to access reliability correctly. By studying effect of loading action and time-dependent character of strength, strength of components is taken as a process of independent increments and autocorrelation coefficient of strength is also calculated, meanwhile, we present a method for computing time-dependent reliability. Combining the reliability design theory with sensitivity analysis method, a numerical method for time-dependent reliability sensitivity design of components based on fully stochastic process is proposed, the variation rules of reliability sensitivity of parameters are given at any moment and the effects of design parameters on reliability of components are also studied. The method presented in this paper provides the theoretical basis for structural design and life prediction of mechanical components.