Uniaxial and Mixed Orientations of Poly(ethylene oxide) in Nanoporous Alumina Studied by X-ray Pole Figure Analysis

The orientation of polymers under confinement is a basic, yet not fully understood phenomenon. In this work, the texture of poly(ethylene oxide) (PEO) infiltrated in nanoporous anodic alumina oxide (AAO) templates was investigated by X-ray pole figures. The influence of geometry and crystallization conditions, such as pore diameter, aspect ratio, and cooling rates, was systematically examined. All the samples exhibited a single, volume-dependent crystallization temperature (Tc) at temperatures much lower than that exhibited by bulk PEO, indicating “clean” microdomains without detectable heterogeneous nucleation. An “orientation diagram” was established to account for the experimental observations. Under very high cooling rates (quenching), crystallization of PEO within AAO was nucleation-controlled, adopting a random distribution of crystallites. Under low cooling rates, growth kinetics played a decisive role on the crystal orientation. A relatively faster cooling rate (10 °C/min) and/or smaller pores lead to the * ║ pore axis (n⃗) mode (uniaxial orientation). When the cooling rate was lower (1 °C/min), and/or the pores were larger, a mixed orientation, with a coexistence of * ║ n⃗ and * ║ n⃗ , was observed. The results favor the kinetic model where the fastest growth direction tends to align parallel to the pore axis.This work is supported by the National Natural Science Foundation of China (NSFC, 21873109, 51820105005, 21274156). G. L. is grateful to the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2015026). G. L., D. W., and A. J. M. also acknowledge European funding by the RISE BIODEST project (H2020-MSCA-RISE-2017-778092). The authors thank Dr. Zhongkai Yang for assistance with pole figure measurement

Method of Image Quality Improvement for Atmospheric Turbulence Degradation Sequence Based on Graph Laplacian Filter and Nonrigid Registration

It is challenging to restore a clear image from an atmospheric degraded sequence. The main reason for the image degradation is geometric distortion and blurring caused by turbulence. In this paper, we present a method to eliminate geometric distortion and blur and to recover a single high-quality image from the degraded sequence images. First, we use optical flow technology to register the sequence images, thereby suppressing the geometric deformation of each frame. Next, sequence images are summed by a temporal filter to obtain a single blurred image. Then, the graph Laplacian matrix is used as the cost function to construct the regularization term. The final clear image and point spread function are obtained by iteratively solving the problem. Experiments show that the method can effectively eliminate the distortion and blur, restore the image details, and significantly improve the image quality

Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks

Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images

Lensless Computational Imaging Technology Using Deep Convolutional Network

Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction

Efficient learning-based blur removal method based on sparse optimization for image restoration.

In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications

The performance analysis of a micro-/nanoscaled quantum heat engine

A new model of micro-/nanoscaled heat engines consisting of two thin long tubes with the same length but different sizes of cross section, which are filled up with ideal quantum gases and operated between two heat reservoirs, is put forward. The working fluid of the heat engine cycle goes through four processes, which include two isothermal processes and two isobaric processes with constant longitudinal pressure. General expressions for the power output and efficiency of the cycle are derived, based on the thermodynamic properties of confined ideal quantum gases. The influence of the size effect on the power output and efficiency is discussed. The differences between the heat engines working with the ideal Bose gas and Fermi gas are revealed. The performance of the heat engines operating at weak gas degeneracy and high temperatures is further analyzed. The results obtained are more general and significant than those in the current literature. (C) 2012 Elsevier B.V. All rights reserved.Specialized Research Fund for the Doctoral Program of Higher Education [20100121110024]; National Natural Science Foundation, People's Republic of China [11175148

Content-illumination coupling guided low-light image enhancement network

Abstract Current low-light enhancement algorithms fail to suppress noise when enhancing brightness, and may introduces structural distortion and color distortion caused by halos or artifacts. This paper proposes a content-illumination coupling guided low-light image enhancement network (CICGNet), it develops a truss topology based on Retinex as backbone to decompose low-light image component in an end-to-end way. The preservation of content features and the enhancement of illumination features are carried out along with depth and width direction of the truss topology. Each submodule uses the same resolution input and output to avoid the introduction of noise. Illumination component prevents misestimation of global and local illumination by using pre- and post-activation features at different depth levels, this way could avoid possible halos and artifacts. The network progressively enhances the illumination component and maintains the content component stage-by-stage. The proposed algorithm demonstrates better performance compared with advanced attention-based low-light enhancement algorithms and state-of-the-art image restoration algorithms. We also perform extensive ablation studies and demonstrate the impact of low-light enhancement algorithm on the downstream task of computer vision. Code is available at: https://github.com/Ruini94/CICGNet