Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as input and outputs the image difference as residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as input and outputs the deblurred version. To more effectively restore image structures such as lines and texts, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks

Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and superresolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a 2-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face-parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy-rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and superresolution

Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning

Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in an improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models

PERFORMANCE EVALUATION OF HYBRID SOLAR AIRWATER HEATER WITH VARIOUS INLET AIR TEMPERATURE DURING HEATING PROCESS

Research about hybrid solar air-water heater that can heating both air and liquid has been conducted for enhancing the usage of solar thermal energy. In the previous study, thermal efficiency of this collector was investigated with many operating and external conditions, but all of previous experiment conducted using outdoor air as inlet air of collector. Thus, in this study, the performance change of hybrid solar air-water heater was investigated with change of inlet air temperature during air and liquid were heated simultaneously. As a result, thermal efficiency for liquid heating was increased with increment of the inlet air temperature. On the contrary to this, thermal efficiency for air heating of collector was decreased with increment of inlet air temperature. In case of total thermal efficiency of collector considered air and liquid heat gain, it was also decreased with increment of inlet air temperature. From these results, it was confirmed that using outdoor air directly as inlet air of collector is better for the use of solar energy. However it is hard to conclude that which is better between using outdoor air and heated air on the perspective of energy saving of building because heat storage performance was increased if the return air or any heated air is used as inlet air of hybrid solar air-water heater when air and liquid was heated simultaneously even air and total thermal efficiency is decreased. Thus, the necessity of more profound study and consideration about this as a further study was also confirmed

Design, Analysis and Empirical Researches for Solar Heat Collecting System based on Flat Mirrors Combination

There has been a dramatic increase of research on energy production using solar energy. This research aims to examine development of concentrating solar collector that is related to mid-high solar energy field. Although the use of dish type solar thermal system has been common in the existing high-efficiency collector technology, several problems have been raised. In order to solve these issues, the frame has been designed as flat plate type with Fresnel lens and the structural stability has been proved by analysis. Furthermore, the experiment that checks collectorrsquos temperature has been performed for the correct works of the stirling engine

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure

In-Plane Strengthening of Unreinforced Masonry Walls by Glass Fiber-Reinforced Polyurea

Strengthening techniques have been employed in Korea to unreinforced masonry walls (UMWs) for several years to protect them from damage caused by the intermittent occurrence of earthquakes. Polyurea, which has a high tensile strength and elongation rate, can be utilized as a strengthening material to enhance the in-plane strength and ductility of UMWs. Glass fiber-reinforced polyurea (GFRPU) is a composite elastomer manufactured by progressively adding milled glass fiber to polyurea. The purpose of this study is to investigate the enhancement of the in-plane strength and ductility of UMWs using GFRPU, depending on the shape of the GFRPU coating on the wall. Four masonry wall specimens are tested with test variables of the number of strengthening sides and coating shapes. It is illustrated that the GFRPU reinforcement of masonry wall leads to enhanced load-carrying capacity, ductility, and energy absorption. An empirical formula to represent the degree of strengthening effected by GFRPU is proposed in this study. Doi: 10.28991/cej-2021-03091782 Full Text: PD

Out-of-Plane Strengthening of Unreinforced Masonry Walls by Glass Fiber-Reinforced Polyurea

Fiber-reinforced polymer reinforcement or polyurea reinforcement techniques are applied to strengthen unreinforced masonry walls (UMWs). The purpose of this experimental study is to verify the out-of-plane reinforcing effect of sprayed glass fiber-reinforced polyurea (GFRPU), which is a composite elastomer made of polyurea and milled glass fibers on UMW. The out-of-plane strengths and ductile behaviors based on various coating shapes are compared in this study. An empirical formula to describe the degree of reinforcement on the out-of-plane strength of the UMW is derived based on the experimental results. It is observed that the peak load-carrying capacity, ductility, and energy absorption capacity gradually improve with an increase in the strengthening degree or area. Compared with the existing masonry wall reinforcement method, the GFRPU technique is a construction method that can help improve the safety performance along with ease of construction and economic efficiency. Doi: 10.28991/CEJ-2022-08-01-011 Full Text: PD