Shu Fujita

Switching dual kernels for separable edge-preserving filtering

In this paper, we propose an accurate approximation framework for separable edge-preserving filtering. Naïve implementation of edge-preserving filtering, such as bilateral filtering and non-local means filtering, consumes enormous computational costs. Separable implementation of such filters is an efficient approximation method for real-time filtering. The accuracy of the conventional separable representation, however, is inadequate when the kernel size is immense. To improve the accuracy, we prepare dual kernels that have different kernel weights for horizontal and vertical filtering of separable filtering. In the experiment, we validate the proposed implementation by using three kinds of filters; bilateral filtering, dual bilateral filtering, and non-local means filtering. Experimental results show that the proposed implementation has higher accuracy while the computational time is almost the same. Moreover, the proposed implementation is practical for denoising and disparity map refinement applications.

Efficient edge-awareness propagation via single-map filtering for edge-preserving stereo matching

In this paper, we propose an efficient framework for edge-preserving stereo matching. Local methods for stereo matching are more suitable than global methods for real-time applications. Moreover, we can obtain accurate depth maps by using edge-preserving filter for the cost aggregation process in local stereo matching. The computational cost is high, since we must perform the filter for every number of disparity ranges if the order of the edge-preserving filter is constant time. Therefore, we propose an efficient iterative framework which propagates edge-awareness by using single time edge preserving filtering. In our framework, box filtering is used for the cost aggregation, and then the edge-preserving filtering is once used for refinement of the obtained depth map from the box aggregation. After that, we iteratively estimate a new depth map by local stereo matching which utilizes the previous result of the depth map for feedback of the matching cost. Note that the kernel size of the box filter is varied as coarse-to-fine manner at each iteration. Experimental results show that small and large areas of incorrect regions are gradually corrected. Finally, the accuracy of the depth map estimated by our framework is comparable to the state-of-the-art of stereo matching methods with global optimization methods. Moreover, the computational time of our method is faster than the optimization based method.

Nearly lattice-matched InAlN/AlGaN two-dimensional electron gas heterostructures grown by metalorganic chemical vapor deposition

Nearly lattice-matched InAlN/AlxGa1−xN (x = 0.1, 0.21, and 0.34) heterostructures with a 1-nm-thick AlN interfacial layer were grown on AlN/sapphire templates by metalorganic chemical vapor deposition. Capacitance–voltage and Hall effect measurements revealed that two-dimensional electron gases (2DEGs) with high densities exceeding 2 × 1013/cm2 were generated at the heterointerface for all samples. It was confirmed that the generation of high-density 2DEGs can be explained as being due to internal polarization effects. The sheet resistance increased from 1,267 to 1,919 Ω/sq with the increase in Al content in the AlGaN channel, owing to the decreases in 2DEG density and mobility.

Randomized redundant DCT: efficient denoising by using random subsampling of DCT patches

j In this paper, we propose an acceleration method for image denoising with a redundant discrete cosine transform (R-DCT). Image denoising is essential for image processing, and its efficiency is important for graphics applications. R-DCT with a hard-thresholding or shrinkage method can perform denoising while keeping detail textures. Moreover, the method is computationally efficient compared with state-of-the-art denoising methods, such as BM3D. The computational cost, however, is still insufficient for real-time processing; hence, we accelerate the method by using randomized subsampling of DCT patches. Experimental results show that our method can accelerate the processing while the degradation of denoising performance is a little.

Cost volume refinement filter for post filtering of visual corresponding

In this paper, we propose a generalized framework of cost volume refinement filtering for visual corresponding problems. When we estimate a visual correspondence map, e.g., depth map, optical flow, segmentation and so on, the estimated map often contains a number of noises and blurs. One of the solutions for this problem is post filtering. Edge-preserving filtering, such as joint bilateral filtering, can remove the noises, but it causes blurs on object boundaries at the same time. As an approach to remove noises without blurring, there is cost volume refinement filtering (CVRF) that is an effective solution for the refinement of such labeling of correspondence problems. There are some papers that propose several methods categorized into CVRF for various applications. These methods use various reconstructing metrics functions, which are L1 norm, L2 norm or exponential function, and various edge-preserving filters, which are joint bilateral filtering, guided image filtering and so on. In this paper, we generalize these factors and add range-spacial domain resizing factor for CVRF. Experimental results show that our generalized formulation outperform the conventional approaches, and also show what the format of CVRF is appropriate for various applications of stereo matching and optical flow estimation.

Demonstration of NOx gas sensing for Pd/ZnO/GaN heterojunction diodes

In this study, planar Pd/ZnO/GaN heterojunction diodes (HJDs) are fabricated and their capabilities for NOx (NO and NO2) gas-sensing is evaluated. The fabricated HJDs exhibit good rectifying properties at a high temperature of 250 °C and, in addition, they exhibit obvious current changes even under low-concentration 10 ppm NOx gases and respond to the on/off switching of the gas introduction. It is considered that the sensor action is owing to the electron depletion around the heterojunction caused by the absorbed gas molecules. The current changes reached relatively high values of approximately 1 mA even under exposure to a low-concentration 10 ppm NO2 gas.

Erratum: “Numerical and experimental analyses on two-dimensional electron mobility in Al(In,Ga)N/AlGaN heterostructures”

Two-dimensional electron mobilities in AlGaN-channel nitride heterostructures were numerically and experimentally analyzed. The calculation and experimental results indicated that mobility for InAlN/AlGaN heterostructures grown by metalorganic chemical vapor deposition was limited by interface roughness scattering as well as alloy disorder scattering. The mobility limits for AlGaN-channel heterostructures with a two-dimensional electron gas (2DEG) density of 3 × 1013/cm2 were estimated to be, for instance, 249 cm2 V−1 s−1 for an Al0.3Ga0.7N channel and 192 cm2 V−1 s−1 for an Al0.5Ga0.5N channel. The calculated values showed no major disagreement with the results reported on AlGaN-channel heterostructures.

High-dimensional Guided Image Filtering

We present high-dimensional filtering for extending guided image filtering. Guided image filtering is one of edge-preserving filtering, and the computational time is constant to the size of the filtering kernel. The constant time property is essential for edge-preserving filtering. When the kernel radius is large, however, the guided image filtering suffers from noises because of violating a local linear model that is the key assumption in the guided image filtering. Unexpected noises and complex textures often violate the local linear model. Therefore, we propose high-dimensional guided image filtering to avoid the problems. Our experimental results show that our high-dimensional guided image filtering can work robustly and efficiently for various image processing.

Extending Guided Image Filtering for High-Dimensional Signals

This paper presents an extended method of guided image filtering (GF) for high-dimensional signals and proposes various applications for it. The important properties of GF include edge-preserving filtering, local linearity in a filtering kernel region, and the ability of constant time filtering in any kernel radius. GF can suffer from noise caused by violations of the local linearity when the kernel radius is large. Moreover, unexpected noise and complex textures can further degrade the local linearity. We propose high-dimensional guided image filtering (HGF) and a novel framework named combining guidance filtering (CGF). Experimental results show that HGF and CGF can work robustly and efficiently for various applications in image processing.