Hyunjung Shim

PURPOSE: Convolutional neural network (CNN)-based image denoising techniques have shown promising results in low-dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel-level loss function. Perceptual loss and adversarial loss have been proposed recently to further improve the image denoising performance. In this paper, we investigate the effect of different loss functions on image denoising performance using task-based image quality ass

We present a new approach to face relighting by jointly estimating the pose, reflectance functions, and lighting from as few as one image of a face. Upon such estimation, we can synthesize the face image under any prescribed new lighting condition. In contrast to commonly used face shape models or shape-dependent models, we neither recover nor assume the 3-D face shape during the estimation process. Instead, we train a pose- and pixel-dependent subspace model of the reflectance function using a

PURPOSE: In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images. METHODS: We first showed that the CNN-based model observer provided similar detection performance to the ideal observer (IO) for signal-known-exactly and background-known-exactly detection tasks with an uncorrelated Gaussian background noise image. We then demonstrated that a single-layer CNN without a nonlinear activation function provided similar de

Translucency introduces great challenges to 3-D acquisition because of complicated light behaviors such as refraction and transmittance. In this paper, we describe the development of a unified 3-D data acquisition framework that reconstructs translucent objects using a single commercial time-of-flight (ToF) camera. In our capture scenario, we record a depth map and intensity image of the scene twice using a static ToF camera; first, we capture the depth map and intensity image of an arbitrary ba

A time-of-flight (ToF) depth sensor produces noisy range data due to scene properties such as surface materials and reflectivity. Sensor measurement frequently includes either a saturated or severely noisy depth and effective depth accuracy is far below its ideal specification. In this paper, we propose a hybrid exposure technique for depth imaging in a ToF sensor so to improve the depth quality. Our method automatically determines an optimal depth for each pixel using two exposure conditions. T

This paper presents a novel approach to automatic face modeling for realistic synthesis from an unknown face image, using a probabilistic face diffuse model and a generic face specular map. We construct a probabilistic face diffuse model for estimating the albedo and normals of the input face. Then, we develop a generic face specular map for estimating the specularity of face. Using the estimated albedo, normal and specular information, we can synthesize the face under arbitrary lighting and vie

A light probe is commonly used for measuring the illumination of a real scene. Instead of equipping a man-made light probe such as a mirror ball, we propose to use a face in images as a natural light probe. To that end, we construct a statistical reflectance model for faces and use this model to extract the lighting and the reflectance field of an input face. With an iterative procedure, we can obtain the lighting condition from an unknown face image. As a byproduct of this procedure, we also es

Time-of-flight (ToF) and structured light depth cameras capture dense three-dimensional (3-D) geometry that is of great benefit for many computer vision problems. For the past couple of years, depth image based gesture recognition, 3-D reconstruction, and robot localization have received explosive interest in the literature. However, depth measurements present unique systematic errors, specifically when objects have specularity or translucency. We present a quantitative evaluation and analysis o