Submillimeter Endoscope Imaging with Sparse Modeling
総務省 戦略的情報通信研究開発推進事業(SCOPE:研究代表者 奥田正浩)

Ministry of Internal Affairs and Communications, Strategic Information and Communications R&D Promotion Programme (Principal Researcher: Masahiro Okuda)

Multi-Contrast Restoration for Medical Imaging
環境技術研究所 重点研究推進支援プロジェクト(研究代表者 奥田正浩)

We develop a tool for multi-contrast image restoration. We restore a various kinds of details from noisy or low-resolution images.

High Dynamic Range Imaging

"Weight Optimization for Multiple Image Integration and Its Applications,"
Ryo Matsuoka, Tomohiro Yamauchi, Tatsuya Baba, Masahiro Okuda,
IEICE Transactions on Information and Systems, Vol.E99-D,No.1,pp.228-235,Jan. 2016,

We propose an image restoration technique that uses multiple image integration. The detail of the dark area when acquiring a dark scene is often deteriorated by sensor noise. Simple image integration inherently has the capability of reducing random noises, but it is especially insufficient in scenes that have a dark area. We introduce a novel image integration technique that optimizes the weights for the integration.

Sparse Modeling for Multi-channel Spectral Restoration

(Collaborative work with Dr.Keiichiro Shirai)

"Local Spectral Component Decomposition for Multi-channel Image Denoising,"
Mia Rizkinia, Tatsuya Baba, Keiichiro Shirai, Masahiro Okuda,
IEEE Trans. on Image Processing, accepted for publication

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.

Compressive sensing and Image restoration

(Collaborative work with Dr.Keiichiro Shirai and Dr.Shunsuke Ono)

"Vectorial total variation based on arranged structure tensor for multichannel image restoration," (PDF)
Shunsuke Ono, Keiichiro Shirai, Masahiro Okuda,
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, pp. 4528-4532, Mar. 2016

We propose a new regularization function, named as Arranged Structure tensor Total Variation (ASTV), for multichannel image restoration. ASTV is based on an arranged structure tensor that becomes an approximately low-rank matrix when a multichannel image of interest has strong correlation among their channels. This observation suggests that penalizing the nuclear norm of the arranged structure tensor is a reasonable regularization for multichannel images, leading to the definition of ASTV.

Misaligned Image Integration

Project Page (Collaborative work with Dr.Keiichiro Shirai)

"Misaligned Image Integration with Local Linear Model,"
Tatsuya Baba, Ryo Matsuoka, Keiichiro Shirai, Masahiro Okuda,
IEEE Trans. on Image Processing, Vol.25, Issue 5, pp.2035-2044, May 2016

We present a new image integration technique for flash and long-exposure image pairs to capture a dark scene without incurring blurring or noisy artifacts. We formulate 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 the images do not need perfect alignment by virtue of our new integration principle.

Reflectance Estimation using convex optimization

"White Balancing by Using Multiple Images via Intrinsic Image Decomposition,"
Ryo Matsuoka, Tatsuya Baba, Masahiro Okuda,
IEICE Transactions on Information and Systems,Vol.E98-D,No.8,pp.1562-1570,Aug. 2015

Using a flash/no-flash image pair, we propose a novel white-balancing technique that can effectively correct the color balance of a complex scene under multiple light sources. In the proposed method, by using multiple images of the same scene taken under different light- ing conditions, we estimate the reflectance component of the scene and the multiple shading components of each image. The reflectance compo- nent is a specific object color which does not depend on scene illumination and the shading component is a shading effect caused by the illumination lights. Then, we achieve white balancing by appropriately correcting the estimated shading components.

Reference-based Image Filtering

(Collaborative work with Dr.Keiichiro Shirai)

"Local Covariance Filtering for Color Images,"
K. Shirai, M, Okuda, T. Jinno, M. Okamoto, M. Ikehara,
AFCV Asian Conf. on Comp. Vision (ACCV), 2012 Nov., accepted

We introduce a novel edge-aware filter that manipulates the local covariances of a color image. A covariance matrix obtained at each pixel is decomposed by the singular value decomposition (SVD), then diagonal eigenvalues are filtered by characteristic control functions. Our filter form generalizes a wide class of edge-aware filters. Once the SVDs are calculated, users can control the filter characteristic graphically by modifying the curve of the characteristic control functions, just like tone curve manipulation while seeing a result in real-time. We also introduce an efficient iterative calculation of the pixel-wise SVD which is able to significantly reduce its execution time.

3D imaging

3D mesh parameterization is a method which converts the complicated 3D mesh into the flat and non-overlapped 2D mesh, and is used for "texture-mapping" to make the correspondence between a texture-image and a 3D mesh in 2D space, and "remeshing" to convert irregular meshes into more manageable meshes. In this paper, we propose a 3D mesh parameterization method which is able to express more detailed shape of the 3D model. However, this one-sided emphasis on "remeshing" incurs texture-distortions in practice. So, we also propose a texture-mapping method which uses a transform-map of texture-coordinates to keep "texture-mapping" qualities.


横尾 彩加


Sparse Filters

Ryo Matsuoka and Masahiro Okuda
SIPシンポジウム, 2012

In this paper, we present a numerical algorithm for the design of FIR filters. Our method minimizes the number of nonzero entries in the impulse response together with the least squares error of its frequency response. We show that the FIR filters with sparse coefficients can outperform a conventional least suares approach and the Parks-McCllelan method under the condition of the same number of multipliers.