低分辨率图像的妆容迁移算法

现有的妆容迁移算法效果优越, 功能丰富, 但是较少考虑到输入图像为低分辨率的场景. 当高分辨率图像难以获得时, 现有的妆容迁移算法将难以适用, 妆容无法完全迁移. 为此本文提出了一种适用于低分辨率图像的妆容迁移算法, 将包含妆容信息的特征矩阵作为先验信息, 将超分辨率网络与妆容迁移网络结合在一起产生协同效应, 即使输入的图像为低分辨率图像, 也能输出高分辨率的妆容迁移结果, 并且充分保留妆容细节的同时提升姿势和表情的鲁棒性. 由于使用端到端的模型实现妆容迁移与超分辨率, 因此设计了一组联合损失函数, 包括生成对抗损失、感知损失、循环一致性损失、妆容损失和均方误差损失函数. 所提出的模型在妆容迁移与超分辨率的定性实验和定量实验中均达到了先进水平.     

有序金纳米棒阵列的超分辨成像

显微成像技术受限于光学成像系统的衍射极限，无法分辨亚波长尺度的结构。通过饱和散射抑制成像技术已经实现了单个纳米颗粒的超分辨成像，但是涉及到纳米颗粒集合，需要考虑纳米颗粒间的耦合作用。利用超越衍射极限的双光束方法，可以在有序金纳米棒阵列上实现远场超分辨光学成像。本文设计了纳米棒长径比为2的5×5金纳米棒阵列，通过矢量光场理论和热扩散理论计算了金纳米棒阵列在连续波激光下的热分布，并模拟了双光束激光即脉冲激发光和连续波抑制光下的散射成像。仿真结果显示，连续波激光能够有效抑制金纳米棒阵列对脉冲激光的散射，双光束方法实现了80 nm横向特征尺寸的超分辨成像。     

Multi-scale convolutional attention network for lightweight image super-resolution

Convolutional neural network (CNN) based methods have recently achieved extraordinary performance in single image super-resolution (SISR) tasks. However, most existing CNN-based approaches increase the model’s depth by stacking massive kernel convolutions, bringing expensive computational costs and limiting their application in mobile devices with limited resources. Furthermore, large kernel convolutions are rarely used in lightweight super-resolution designs. To alleviate the above problems, we propose a multi-scale convolutional attention network (MCAN), a lightweight and efficient network for SISR. Specifically, a multi-scale convolutional attention (MCA) is designed to aggregate the spatial information of different large receptive fields. Since the contextual information of the image has a strong local correlation, we design a local feature enhancement unit (LFEU) to further enhance the local feature extraction. Extensive experimental results illustrate that our proposed MCAN can achieve better performance with lower model complexity compared with other state-of-the-art lightweight methods.     

基于多尺度注意力残差网络的图像超分辨率重建

数字图像在传递信息中起着重要的作用,图像超分辨率技术能丰富图像的细节信息.针对许多网络对低分辨率图像的有效特征复用不足和参数量过大的问题,本文结合不同大小的卷积核以及注意力残差机制构建图像超分辨率网络,用3个有差别尺度的卷积层来提取图像的特征,其中第2和第3层用小卷积核替代大的卷积核,对3层卷积融合之后引入注意力机制,...     

超分辨测向中奇异值分解的FPGA实现

奇异值分解是超分辨测向技术的核心组成部分,现有的并行实现方案适用范围窄,运算量大,迭代时间长.为了满足测向接收机系统的高实时性需求,结合双边Jacobi算法的交换策略和单边Jacobi算法的求角结构,提出了一种改进的实现方法.该实现方法修正了脉动阵列的收敛性问题,提高了复数矩阵的收敛速度.同时,给出了算法的现场可编程门阵列(FPGA)实现结构.仿真结果证明该方案耗时在百微秒以内,能够应用于电子侦察设备.     

Contrastive Learning for Blind Super-Resolution via A Distortion-Specific Network

Previous deep learning-based super-resolution (SR) methods rely on the assumption that the degradation process is predefined (e.g., bicubic downsampling). Thus, their performance would suffer from deterioration if the real degradation is not consistent with the assumption. To deal with real-world scenarios, existing blind SR methods are committed to estimating both the degradation and the super-resolved image with an extra loss or iterative scheme. However, degradation estimation that requires more computation would result in limited SR performance due to the accumulated estimation errors. In this paper, we propose a contrastive regularization built upon contrastive learning to exploit both the information of blurry images and clear images as negative and positive samples, respectively. Contrastive regularization ensures that the restored image is pulled closer to the clear image and pushed far away from the blurry image in the representation space. Furthermore, instead of estimating the degradation, we extract global statistical prior information to capture the character of the distortion. Considering the coupling between the degradation and the low-resolution image, we embed the global prior into the distortion-specific SR network to make our method adaptive to the changes of distortions. We term our distortion-specific network with contrastive regularization as CRDNet. The extensive experiments on synthetic and real-world scenes demonstrate that our lightweight CRDNet surpasses state-of-the-art blind super-resolution approaches.