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Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance. On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed. These extensions focus on the pixel-wise correlation among color channels. However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels. As a result, color artifacts appear in a denoised image obtained by using the existing methods. In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN). The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior. The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches. It enables us to naturally represent the higher-order dependencies among similar color patches. We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm. The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.

Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank—e.g., the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e.g., the sum of nuclear norm, weighted tensor nuclear norm, and weighted tensor schatten-p norm. In particular, the weighted tensor schatten-p norm has two parameters, the weight and p, and the sum of nuclear norm and weighted tensor nuclear norm are special cases of these parameters. However, there has been no detailed discussion of whether the effects of the weighting and p are synergistic. In this paper, we propose a novel low-rank tensor completion model using the weighted tensor schatten-p norm to reveal the relationships between the weight and p. To clarify whether complex methods such as the weighted tensor schatten-p norm are necessary, we compare them with a simple method using rank-constrained minimization. It was found that the simple methods did not outperform the complex methods unless the rank of the original tensor could be accurately known. If we can obtain the ideal weight, p=1 is sufficient, although it is necessary to set p&lt, 1 when using the weights obtained from observations. These results are consistent with existing reports.

Summary Ground-penetrating radar (GPR) is a geophysical method for non-destructive inspection of underground infrastructure. The main impediment to machine-learning-based classification of GPR data is gathering enough labeled data. Previous work done to solve this problem generated pseudo-GPR data through the numerical simulations done with expensive GPU clusters. In this paper, we propose a simple yet effective framework for machine learning with GPR data without enormous computational cost. The key idea of our method is quasi-measurement pitch changes (QMPC) that can obtain several times the amount of pseudo-data from real measurements. QMPCs are based on a simple sub-sampling procedure from real data, and no interpolation is applied for the pseudo-data. Thus, special hardware like GPU clusters are not required and no artifacts are produced by such an interpolation. Moreover, using QMPCs for test data allows us to apply ensemble learning at the inference phase of machine learning. The experimental results for the classification problem of buried objects clearly show that our framework can drastically improve accuracy with additional labeled data and without significantly increasing computational cost.

We propose a new color imaging system based on a compressive sensing technique. Our system consists of a random complementary color filter array (CFA) for random projection and a color reconstruction method for demosaicing. Our CFA overlaps two complementary color filters and consists of six color filters: cyan (C), yellow (Y), magenta (M), C+Y, C+M, and Y+M. By arranging these six color filters randomly, our imaging system achieves pseudo random projection among red (R) /green (G) / blue (B) colors, which is the key technology of compressive sensing. Because this CFA can retain more color information than RGB CFA, the proposed color reconstruction method reduces artifacts at monochromatic edges and in high-frequency regions, and obtains better image quality. As an additional contribution, we introduce saturation consistency to suppress color artifacts in saturated areas, then achieve to 3.3 dB higher quality images than the conventional method.

Recently, a novel approach to color image compression based on colorization has been presented. The conventional method for colorization-based image coding tends to lose the local oscillation of chrominance components that the original images had. A large number of color assignments is required to restore these oscillations. On the other hand, previous studies suggest that an oscillation of a chrominance component correlates with the oscillation of a corresponding luminance component. In this paper, we propose a new colorization-based image coding method that utilizes the local correlation between texture components of luminance and chrominance. These texture components are obtained by a total variation regularized energy minimization method. The local correlation relationships are approximated by linear functions, and their coefficients are extracted by an optimization method. This key idea enables us to represent the oscillations of chrominance components by using only a few pieces of information. Experimental results showed that our method can restore the local oscillation and code images more efficiently than the conventional method, JPEG, or JPEG2000 at a high compression rate.

Many content distribution systems such as CDN and P2P file sharing have been developed. In these systems, file-type contents require downloads to be completed before they can be played and they have no value before the download finishes. Therefore, a user's satisfaction depends on the length of the service latency. That is, the length of time from when the user issued a request until the user received an entire file. Reducing the sum of that time is necessary for the whole delivery system to satisfy users and maintain dependability on system performance. We discuss a hop-by-hop file delivery system suitable for delivering file contents whereby the sum of service latency is reduced by using the request conditions. Moreover, we propose a file delivery scheduling algorithm for a one-link model given that the content request frequency is unknown. The algorithm is based on a local optimal strategy. We performed a characteristic analysis by computer simulation. The results showed that our algorithm performs at nearly the theoretical efficiency limit of the hop-by-hop system when the request frequency distribution of each content has a deviation.

Novel image compression approaches that use a process called colorization, which is a method for adding color to a monochrome image, has recently been proposed. Almost all the proposed methods represent color information by using a set of color points. However, in an area with flat chrominance and variant luminance, we need redundant points to correctly decode color. We propose a color information extraction method that represents color information as a set of color lines. To effectively extract lines from an input image, color clustering and tentative color information have been introduced. The experimental results show that our proposed method allows us to drastically suppress the color information compared to existing colorization-based coding and conventional JPEG compression.

Many content distribution systems such as CDN and P2P file sharing have been developed. In these systems, file-type contents require downloads to be completed before they can be played and they have no value before the download finishes. Therefore, the total download time for this sort of content is very important, and the service period should be reduced to satisfy users. We discuss a hop-by-hop file delivery system suitable for delivering file contents whereby the sum of service periods is reduced by using the request conditions. Moreover, we propose a file delivery scheduling algorithm for a one-link model given that the content request frequency is unknown. The algorithm is based on a local optimal strategy. We performed a characteristic analysis by computer simulation. The results showed that our algorithm performs to reduce the sum of the service periods in the hop-by-hop system effectively when request frequency distribution of each content has a deviation.