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. We find the optimal weight map by solving a convex optimization problem for the weight optimization. Additionally, we apply the proposed weight optimization scheme to a single-image super-resolution problem, where we slightly modify the weight optimization problem to estimate the high-resolution image from a single low-resolution one. We use some of our experimental results to show that the weight optimization significantly improves the denoising and super-resolution performances.

An enzymatic biofuel cell (EBFC) is a device that uses an enzyme as a catalyst to convert chemical energy into electrical energy by a redox reaction to generate electricity. EBFC has the advantage that it can operate under mild conditions (normal temperature, normal pressure, and near neutral pH) and can use various energy sources such as sugar and alcohol. Hoshi et al. reported EBFC of glucose fuel using graphene-coated carbon fiber cloth (GCFC) with a large specific surface area. However, it was considered that GOD was affected by dissolved oxygen in the fuel and generated hydrogen peroxide, which hindered the reaction. In order to further increase the output, it was necessary to improve the performance of the anode with a novel enzyme that is less affected by oxygen and generates electricity from glucose. Therefore, we focused on FAD glucose dehydrogenase (FAD-GDH). It can generate electricity with glucose fuel by using it as a catalyst like GOD. Characteristic is that it is resistant to impurities such as maltose and galactose and is not easily affected by oxygen. It was thought that this would alleviate the concern about hydrogen peroxide and improve the output.

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.

We propose an algorithm for the constrained design of FIR filters with sparse coefficients. In general filter design approaches, as the length of the filter increases, the number of multipliers used to construct the filter increases. This is a serious problem, especially in two-dimensional FIR filter designs. The FIR filter coefficients designed by the least-squares method with peak error constraint are optimal in the sense of least-squares within a given order, but not necessarily optimal in terms of constructing a filter that meets the design specification under the constraints on the number of coefficients. That is, a higher-order filter with several zero coefficients can construct a filter that meets the specification with a smaller number of multipliers. We propose a two-step approach to design constrained sparse FIR filters. Our method minimizes the number of non-zero coefficients while the frequency response of the filter that meets the design specification. It achieves better performance in terms of peak error than conventional constrained least-squares designs with the same or higher number of multipliers in both one-dimensional and two-dimensional filter designs.

This paper proposes a method for evaluating visual differences caused by decimation. In many applications it is important to evaluate visual differences of two different images. There exist many image assessment methods that utilize the model of the human visual system (HVS), such as the visual difference predictor (VDP) and the Sarnoff visual discrimination model. In this paper, we extend and elaborate on the conventional image assessment method for the purpose of evaluating the visual difference caused by the image decimation. Our method matches actual human evaluation more and requires less computational complexity than the conventional method.

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 lighting conditions, we estimate the reflectance component of the scene and the multiple shading components of each image. The reflectance component 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. The proposed method achieves better performance than conventional methods, especially under colored illumination and mixed lighting conditions.