Variational Deep Image Restoration.

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations.

Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks.

This article presents a new method for understanding and visualizing convolutional neural networks (CNNs). Most existing approaches to this problem focus on a global score and evaluate the pixelwise contribution of inputs to the score. The analysis of CNNs for multilabeled outputs or regression has not yet been considered in the literature, despite their success on image classification tasks with well-defined global scores.

Co-Salient Object Detection Based on Deep Saliency Networks and Seed Propagation Over an Integrated Graph.

This paper presents a co-salient object detection method to find common salient regions in a set of images. We utilize deep saliency networks to transfer co-saliency prior knowledge and better capture high-level semantic information. The resulting initial co-saliency maps are enhanced by seed propagation steps over an integrated graph.

Does Sacral Slanting Affect Distal Adding-on in Lenke Type 1A Adolescent Idiopathic Scoliosis?

Study Design: A retrospective clinical study.

Objective: The purpose of this study was to identify risk factors for postoperative distal adding-on in Lenke 1A adolescent idiopathic scoliosis.

Summary Of Background Data: Distal adding-on is a postoperative complication associated with the Lenke type 1A curve.

Referral patterns and patient characteristics at the first visit to a scoliosis center: a 2-year experience in South Korea without a school scoliosis-screening program.

OBJECTIVE The need for scoliosis screening remains controversial. Nationwide school screening for scoliosis has not been performed in South Korea, and there are few studies on the referral patterns of patients suspected of having scoliosis. This study aimed to examine the referral patterns to the largest scoliosis center in South Korea in the absence of a school screening program and to analyze the factors that influence the appropriateness of referral.

Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network.

A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes of the secrecy signal of the secondary network (SN) and of the primary signal of the primary network (PN). The harvested energy is split into two parts according to a power splitting ratio, one for decoding the primary signal and the other for charging the battery.

Semi-Circumferential Decompression: Microsurgical Total en-bloc Ligamentum Flavectomy to Treat Lumbar Spinal Stenosis with Grade I Degenerative Spondylolisthesis.

Background: To describe and assess clinical outcomes of the semi-circumferential decompression technique for microsurgical en-bloc total ligamentum flavectomy with preservation of the facet joint to treat the patients who have a lumbar spinal stenosis with degenerative spondylolisthesis.

Methods: We retrospectively analyzed the clinical and radiologic outcomes of 19 patients who have a spinal stenosis with Meyerding grade I degenerative spondylolisthesis. They were treated using the "semi-circumferential decompression" method.

Hierarchical prediction and context adaptive coding for lossless color image compression.

This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels.

Text-line extraction in handwritten Chinese documents based on an energy minimization framework.

Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing.

Design of interchannel MRF model for probabilistic multichannel image processing.

In this paper, we present a novel framework that exploits an informative reference channel in the processing of another channel. We formulate the problem as a maximum a posteriori estimation problem considering a reference channel and develop a probabilistic model encoding the interchannel correlations based on Markov random fields. Interestingly, the proposed formulation results in an image-specific and region-specific linear filter for each site.

Composition of a dewarped and enhanced document image from two view images.

In this paper, we propose an algorithm to compose a geometrically dewarped and visually enhanced image from two document images taken by a digital camera at different angles. Unlike the conventional works that require special equipment or assumptions on the contents of books or complicated image acquisition steps, we estimate the unfolded book or document surface from the corresponding points between two images. For this purpose, the surface and camera matrices are estimated using structure reconstruction, 3-D projection analysis, and random sample consensus-based curve fitting with the cylindrical surface model.