Sandwiched assembly of ZnO nanowires between graphene layers for a self-powered and fast responsive ultraviolet photodetector

A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

Sandwiched assembly of ZnO nanowires between graphene layers for a self-powered and fast responsive ultraviolet photodetector

A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

On Fast Bilateral Filtering Using Fourier Kernels

It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the nonlinear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee subpixel accuracy for the overall filtering, which is not provided by the most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.

IMAGE DENOISING USING OPTIMALLY WEIGHTED BILATERAL FILTERS: A SURE AND FAST APPROACH

The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.