Keiichiro Shirai

Local Spectral Component Decomposition for Multi-Channel Image Denoising.

We propose a method for local spectral component decomposition based on the line feature of local distribution. Our aim is to reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. We first calculate a linear feature over the spectral components of an M-channel image, which we call the spectral line, and then, using the line, we decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, and thus our algorithm needs to denoise only the two gray-scale images, regardless of the number of the channels. As a result, image deterioration due to the imbalance of the spectral component correlation can be avoided. The experiment shows that our method improves image quality with less deterioration while preserving vivid contrast. Our method is especially effective for hyperspectral images. The experimental results demonstrate that our proposed method can compete with the other state-of-the-art denoising methods.

Noiseless no-flash photo creation by color transform of flash image

In the dark place photographing, the increase of noise is one of the major problems to be addressed. Although using a flash is effective to reduce the noise, natural colors are faded away due to increase of specular lights. In this paper, we present a method that generates a no-flash like flash image by approximating colors of a flash image by those of a no-flash image. Our method is based on the ”color-line” feature of images, a linear distribution of colors in a local region is transformed by a set of transform matrices automatically. This method is able to deal correctly with the occluded regions where the colors are saturated by reflections or shadows and the color distribution is squashed.

Vectorial total variation based on arranged structure tensor for multichannel image restoration

We propose a new regularization function, named as Arranged Structure tensor Total Variation (ASTV), for multichannel image restoration. Since the standard structure tensor is a matrix whose eigenvalues well encodes local neighborhood information of an image, there has been proposed vectorial total variation based on the structure tensor for image regularization. However, the correlation among the channels cannot be measured by the structure tensor because the discrete differences of all the channels are just summed up in the entries of the structure tensor. On the other hand, ASTV is based on a newly-defined arranged structure tensor that becomes an approximately low-rank matrix when multichannel images have strong correlation among their channels. This suggests that penalizing the nuclear norm of the arranged structure tensor is a reasonable regularization for multichannel images, leading to the definition of ASTV. Experimental results illustrate the advantage of ASTV over a state-of-the-art vectorial total variation based on the structure tensor.

Data tracking and effect of frequency offset to simultaneous collecting method for wireless sensor networks

In the packet access of wireless sensor networks, a distributed access protocol is employed to avoid packet collision but it also causes delay. Therefore, real time data collection is difficult. In wireless communication for simultaneous multidata collection (WC-SDC), sensed data are projected onto the parameters of the wireless communication. The specific feature of the sensed data appears in received signals. Even if the transmitted signal from each sensor collides by simultaneous access, the mixture sensed can be separated by using the specific features. Therefore, the real time data collection is achieved. However, frequency mismatch causes the fluctuation of sensed data, which gives the adverse impact to data separation. In this paper, a data tracking method is used for the data separation in the WC-SDC. We clarify the accuracy of data separation and the impact from the frequency offset. We propose a method for coping with the frequency offset and the error tracking. From the numerical results, our proposed method accurately achieves data separation even under 7% frequency offset normalized by the minimum frequency resolution.

Flash/no-flash image integration using convex optimization

When high ISO sensitivity is used to acquire images of dark scenes, their detail textures are often deteriorated by sensor noise. On the other hand, using flash photography with artificial light, one can shorten the exposure time, and obtain a sharp image under the low ISO sensitivity. However, the use of flash light changes the color tone and often generates unnatural images due to a specific color temperature of the additional light. This paper presents a new efficient method for flash/no-flash image integration. In contrast to conventional integration methods assuming that the flash image has a sharp texture without any noise, our method can successfully remove noise. Specifically, our method separately handles regions within the reach of the flash light and other regions out of range of the flash light, because the two regions have much different characteristics. As for the former well-exposed regions, we transferred the detail of the flash image to no-flash image by optimization and component separation. As for the latter under-exposed regions, an optimization based joint bilateral filtering that uses information of a flash image is performed to remove noise. Experimental results show the effectiveness of our method compared to the conventional methods.

High dynamic range image acquisition using flash image

We propose a denoising technique using multiple exposure image integration. When acquiring a dark scene, the detail of the dark area is often deteriorated by sensor noise. For a high dynamic range image acquisition, denoising in dark areas is a critical issue, since the dark area is, in general, enhanced by a tone-mapping and the noise is made more visible when displaying it on an output devise. In our method, a flash image is utilized as well as no-flash multiple exposure images to further reduce the noise. Multiple exposure integration is performed in a wavelet domain, where noise removal is achieved by the wavelet-shrinkage for multiple exposures. Our method works well especially for noise in shadows. We show the validity of the proposed algorithm by simulating the method with some actual noisy images.

Embedding a Mathematical OCR Module into OCRopus

This paper describes embedding a mathematical formula recognition module into the OCR system OCRopus aiming at developing a OCR system for scientific and technical documents which include mathematical formulas. OCRopus is a open source OCR system emphasizing modularity, easy extensibility, and reuse. This system has several basic components such as preprocessing, layout analysis, and text line recognition, so it is a challenging project to embed the mathematical formula recognition module into the OCRopus system. We have developed the math OCR module, then report how to embed our module into the OCRopus system in order to realize a math OCR which can deal with wide variety of documents including mathematical formulas.

Non-local/local image filters using fast eigenvalue filtering

In this paper, we propose a fast and an approximate solution of non-local/local filters using Chebyshev polynomial approximation (CPA). A non-local/local filter is generally expressible in a matrix form. From the matrix notation, image denoising performance is improved by filtering the eigenvalues of the filter matrix. However, it requires much execution time due to computational complexity of eigendecomposition. To reduce the computational cost, we apply the CPA to eigenvalue filtering, leading to an eigendecomposition-free procedure. Moreover, a fast SURE-based parameter optimization is possible by using the CPA. It enables us to determine a suitable filtering parameter efficiently. Numerical examples illustrate that the proposed method is significantly faster than conventional methods while it maintains high approximate precision.

FFT BASED SOLUTION FOR MULTIVARIABLE L2 EQUATIONS USING KKT SYSTEM VIA FFT AND EFFICIENT PIXEL-WISE INVERSE CALCULATION

When solving l2 optimization problems based on linear filtering with some regularization in signal/image processing such as Wiener filtering, the fast Fourier transform (FFT) is often available to reduce its computational complexity. Most of the problems, in which the FFT is used to obtain their solutions, are based on single variable equations. On the other hand, the Karush-Kuhn-Tucker (KKT) system, which is often used for solving constrained optimization problems, generally results in multivariable equations. In this paper, we propose a FFT based computational method for multivariable l2 equations. Our method applies a FFT to each block of the KKT system, and represents the equation as an image-wise simultaneous equation consisting of Fourier transformed filters and images. In our method, an inverse matrix calculation that consists of complex pixel values gathered from each transformed image is required for each pixel. We exploit the homogeneity of neighboring values and solve them efficiently.

RETARGETING PYRAMID USING DIRECT DECIMATION

The retargeting pyramid (RP) is a multiscale image pyramid using content-aware image resizing. In the previous implementation of the RP, a two-step interpolation is adopted to obtain the desired resolution. However, this interpolation leads to performance loss for image processing. In this paper, we improve the performance of the RP by replacing the two-step interpolation with a single interpolation using a matrix representation of the bilateral filter and Tikhonov regularization.