arXiv subjects

Sayaka Shiota

Publications and source records attributed to Sayaka Shiota.

27 records · Page 2Linked to original sources

arXiv2020

A Privacy-Preserving Machine Learning Scheme Using EtC Images

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.

cs.CR↗

arXiv2019

An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures

This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE enables us not only to produce images without color saturation regions from a single-shot image, but also to avoid ghost artifacts in the producing ones. However, the number of exposures is generally limited to two, and moreover it is difficult to decide the optimum exposure values before the photographing. In the proposed scheme, a scene segmentation method is applied to input multi-exposure images, and then the luminance of the input images is adjusted according to both of the number of scenes and the relationship between exposure values and pixel values. The proposed method with the luminance adjustment allows us to improve the above two issues. In this paper, we focus on dual-ISO imaging as one of single-shot imaging. In an experiment, the proposed scheme is demonstrated to be effective for single-shot high dynamic range imaging with SVE, compared with conventional MEF schemes with exposure compensation.

eess.IV↗

arXiv2019

Single-Shot High Dynamic Range Imaging with Spatially Varying Exposures Considering Hue Distortion

We proposes a novel single-shot high dynamic range imaging scheme with spatially varying exposures (SVE) considering hue distortion. Single-shot imaging with SVE enables us to capture multi-exposure images from a single-shot image, so high dynamic range images can be produced without ghost artifacts. However, SVE images have some pixels at which a range supported by camera sensors is exceeded. Therefore, generated images have some color distortion, so that conventional imaging with SVE has never considered the influence of this range limitation. To overcome this issue, we consider estimating the correct hue of a scene from raw images, and propose a method with the estimated hue information for correcting the hue of SVE images on the constant hue plain in the RGB color space.

eess.IV↗

arXiv2018

A Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function

This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have low contrast in dark or bright regions. This is due to a limited dynamic range that imaging sensors have. For this reason, various contrast enhancement methods have been proposed. Our proposed method can enhance the contrast of images without not only over-enhancement but also noise amplification. In the proposed method, an image is decomposed into illumination layer and reflectance layer based on the retinex theory, and lightness information of the illumination layer is adjusted. A shadow-up function is used for preventing over-enhancement. The proposed mapping function, designed by using a noise aware histogram, allows not only to enhance contrast of dark region, but also to avoid amplifying noise, even under strong noise environments.

cs.CV↗

arXiv2018

Privacy-Preserving SVM Computing by Using Random Unitary Transformation

A privacy-preserving Support Vector Machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as unauthorized use and leak of data, and privacy compromise. We focus on templates protected by using a random unitary transformation, and consider some properties of the protected templates for secure SVM computing, where templates mean features extracted from data. The proposed scheme enables us not only to protect templates, but also to have the same performance as that of unprotected templates under some useful kernel functions. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In the experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.

cs.CR↗

arXiv2018

A Pseudo Multi-Exposure Fusion Method Using Single Image

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.

cs.CV↗

arXiv2018

Multi-Exposure Image Fusion Based on Exposure Compensation

This paper proposes a novel multi-exposure image fusion method based on exposure compensation. Multi-exposure image fusion is a method to produce images without color saturation regions, by using photos with different exposures. However, in conventional works, it is unclear how to determine appropriate exposure values, and moreover, it is difficult to set appropriate exposure values at the time of photographing due to time constraints. In the proposed method, the luminance of the input multi-exposure images is adjusted on the basis of the relationship between exposure values and pixel values, where the relationship is obtained by assuming that a digital camera has a linear response function. The use of a local contrast enhancement method is also considered to improve input multi-exposure images. The compensated images are finally combined by one of existing multi-exposure image fusion methods. In some experiments, the effectiveness of the proposed method are evaluated in terms of the tone mapped image quality index, statistical naturalness, and discrete entropy, by comparing the proposed one with conventional ones.

cs.CV↗

arXiv2018

Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts

It is well-known that a number of excellent super-resolution (SR) methods using convolutional neural networks (CNNs) generate checkerboard artifacts. A condition to avoid the checkerboard artifacts is proposed in this paper. So far, checkerboard artifacts have been mainly studied for linear multirate systems, but the condition to avoid checkerboard artifacts can not be applied to CNNs due to the non-linearity of CNNs. We extend the avoiding condition for CNNs, and apply the proposed structure to some typical SR methods to confirm the effectiveness of the new scheme. Experiment results demonstrate that the proposed structure can perfectly avoid to generate checkerboard artifacts under two loss conditions: mean square error and perceptual loss, while keeping excellent properties that the SR methods have.

cs.CV↗

arXiv2018

Automatic Exposure Compensation for Multi-Exposure Image Fusion

This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce images without saturation regions, by using photos with different exposures. In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them. However, how to determine the degree of adjustment has never been discussed. This paper therefore proposes a way to automatically determines the degree on the basis of the luminance distribution of input multi-exposure images. Moreover, new weights, called "simple weights", for image fusion are also considered for the proposed luminance adjustment method. Experimental results show that the multi-exposure images adjusted by the proposed method have better quality than the input multi-exposure ones in terms of the well-exposedness. It is also confirmed that the proposed simple weights provide the highest score of statistical naturalness and discrete entropy in all fusion methods.

cs.CV↗