Tatsuya Baba

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.

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.

Weight optimization for multiple image integration

We propose a denoising technique using multiple image integration. When acquiring a dark scene, the detail of the dark area is often deteriorated by sensor noise. A simple image integration inherently has the capability of reducing random noises. In this paper we develop the denoising performance of the multiple image integration by optimizing weight maps. We determine the optimal weight by solving a convex optimization problem. Through some experimental results, we show the weight optimization significantly improves the de-noising performance.

Constrained design of FIR filters with sparse coefficients

We present an algorithm for the constrained design of FIR filters with sparse coefficients. In general, the filter design approach aims to minimize a filter order and maximize the filter performance. Although the FIR filter coefficients designed by the least squares method is optimal in the least squares sense, it is not necessarily optimal among the set of filters with the same number of multipliers, that is, less mean squared error can be achieved by a filter that has the same number of multipliers, but has longer impulse response with some zero-valued entries. Our method minimizes the number of nonzero entries in the impulse response together with the least squares error of its frequency response. In addition, we incorporate some constraints to the design and realize better performance than conventional constrained least squares design.

Misaligned Image Integration With Local Linear Model

We present a new image integration technique for a flash and long-exposure image pair to capture a dark scene without incurring blurring or noisy artifacts. Most existing methods require well-aligned images for the integration, which is often a burdensome restriction in practical use. We address this issue by locally transferring the colors of the flash images using a small fraction of the corresponding pixels in the long-exposure images. We formulate the image integration as a convex optimization problem with the local linear model. The proposed method makes it possible to integrate the color of the long-exposure image with the detail of the flash image without causing any harmful effects to its contrast, where we do not need perfect alignment between the images by virtue of our new integration principle. We show that our method successfully outperforms the state of the art in the image integration and reference-based color transfer for challenging misaligned data sets.

Reflectance estimation and white balancing using multiple images.

We propose a novel method for reflectance estimation by using a flash/no-flash image pair. In our method, by using multiple images of the same scene taken under different lighting conditions, we estimate a reflectance component which does not depend on scene illumination, and a shading component caused by illumination lights. Moreover, we apply it to white balance correction by appropriately correcting the estimated shading components. The proposed method achieve better performance than conventional methods especially under colored illumination and mixture lighting conditions.

An automatic yearbook style photo generation method using color grading and guide image filtering based facial skin color correction

In this paper, we propose an automatic photograph generating method for albums of large numbers of people, which gives consistency to facial skin color and background colors. In ourframework, we assume photography in a simple photographic environment where some amateur cameramen take photographs indoors. In such a case, facial skin color is often distorted due to the lighting environment (e.g., light reflected from a colored rear wall), and if the photo is artificially combined with another background, the difference in color is emphasized, resulting in an unnatural synthesized result. In our technique, after roughly extracting the facial region and rectifying the distribution of the skin color in a color space, we perform correction of the color and brightness near the face of the original image so that it matches the color balance ofthefacial image after the color correction. Unlike conventional algorithms for color correction, the final result is attained by a correction process with a guide image. We show through experimental results that more natural results are obtained than conventional methods.