Search

In this paper, we propose a novel random walk model, called Dynamic Random Walk (DRW), which adds a new type of dynamic node to the original RW model and reduces redundant calculation by limiting the walk range. To solve the seed-lacking problem of the proposed DRW, we redefine the energy function of the original RW and use the first arrival probability among each node pair to avoid the interference for each partition. Relaxation of our DRW is performed with the help of a greedy strategy and the Weighted Random Walk Entropy(WRWE) that uses the gradient feature to approximate the stationary distribution. The proposed DRW not only can enhance the boundary adherence but also can run with linear time complexity. To extend our DRW for superpixel segmentation, a seed initialization strategy is proposed. It can evenly distribute seeds in both 2D and 3D space and generate superpixels in only one iteration. The experimental results demonstrate that our DRW is faster than existing RW models and better than the state-of-the-art superpixel segmentation algorithms with respect to both efficiency and segmentation effects. [ABSTRACT FROM AUTHOR]

To ensure the confidentiality of color images during their storage or transmission on insecure networks, a number of encryption methods based on fractional transforms have been proposed and widely investigated. However, most of their outputs are complex values that are inconvenient for record and transmission. Also, those methods always deal with a whole color component with the same fractional-order and ignore the textural features that are contained in different image parts. In this paper, we first define a reality-preserving multiple-parameter fractional Hartley transform (RPMPFRHT), whose output is real value, and then a novel color image encryption method is proposed that divides the RGB components into different blocks and uses a constructed pixel scrambling operator to mix and hide the original color information. The outputs are transformed to different RPMPFRHT domains and further scrambled by a non-adjacent coupled map lattices system. Numerical simulations are performed to demonstrate that the proposed encryption algorithm is feasible, secure, sensitive to keys, and robust to potential attacks. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SR) aims to estimate high-resolution (HR) images from low-resolution (LR) ones with high reconstruction quality and low time cost. In this paper, we propose a new pixel-wise SR method by combining a classification tree of external samples and a learning stage of nonlinear pixel-reconstruction mapping-kernels. To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm. Then, a decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch. In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction. Therefore, our method can simultaneously guarantee the reconstruction quality and execution speed at the stage of sample representation and mapping. Experiment results demonstrate the effectiveness of our approach on quantitative visual quality assessment and time cost. • To obtain more reasonable sample sets for mapping-learning, we classify external samples by clustering the mapping-vector of LR-HR patch pairs based on fractional-norm. • A decision-tree branched by lightweight networks is learned to choose one reasonable class for each testing LR patch. • In our mapping-learning stage, pixel-wise nonlinear mappings are represented as several full connected networks, which can provide satisfying generalization ability for LR patch reconstruction. [ABSTRACT FROM AUTHOR]