LFMCW雷达运动目标距离与速度超分辨估计

超分辨谱估计算法能得到比传统周期图法高得多的分辨率,针对LFMCW雷达动目标检测问题,本文提出一种基于状态矢量空间方法的LFMCW雷达距离与速度超分辨估计方法。文中介绍了状态矢量空间方法的基本原理并分析了三角LFMCW雷达上、下扫频段差频信号的特点,使用基于状态矢量空间方法估计差频信号频谱,同时给出了相应的运动目标距离与速度的估计算法。该算法解决了LFMCW雷达动目标去耦问题,与传统FFT方法相比提高了运动目标距离与速度的分辨率和估计精度。仿真结果证明了该算法的有效性。      

基于MAP算法的图像超分辨率重建

近年来图像的超分辨率重建已经成为人们广泛研究的热点.本文提出了一种从多幅低分辨率欠采样图像中重建出一幅高分辨率图像的重建方法.该方法基于MAP框架,用迭代方法得到最优化解.在每次的迭代过程中利用上次迭代得到的重建图像的有用信息来不断调整迭代参数,不断的循环迭代,最后求解出重建图像的最优解.实验结果证明,该方法有效,它不仅能在迭代过程中自动选择和更新调整参数,并且能得到期望的高分辨率重建图像.     

Variational method for super-resolution optical flow

The motion fields in an image sequence observed by a car-mounted imaging system depend on the positions in the imaging plane. Since the motion displacements in the regions close to the camera centre are small, for accurate optical flow computation in this region, we are required to use super-resolution of optical flow fields. We develop an algorithm for super-resolution optical flow computation. Super-resolution of images is a technique for recovering a high-resolution image from a low-resolution image and/or image sequence. Optical flow is the appearance motion of points on the image. Therefore, super-resolution optical flow computation yields the appearance motion of each point on the high-resolution image from a sequence of low-resolution images. We combine variational super-resolution and variational optical flow computation in super-resolution optical flow computation. Our method directly computes the gradient and spatial difference of high-resolution images from those of low-resolution images, without computing any high-resolution images used as intermediate data for the computation of optical flow vectors of the high-resolution image.     

小波图像融合改善超声图像横向分辨率的研究

摘要：针对超声图像分辨率较低,尤其是横向分辨率低的特点,采用基于插值和小波图像融合相结合的方法对相控阵超声图像进行了多种方式的对比试验,结果表明：算法采用区域能量大小判定的设计思想对改善超声图像的分辨率是可行的,其中利用小波分解将经过插值放大的待融合图像分解为低频部分和高频部分,然后将低频部分采用平均法,高频部分采用区域能量最大的融合方式对改善超声图像的分辨率取得了较好的效果。     

A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes. Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework. In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts. The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.     

A secure fingerprint template generation mechanism using visual secret sharing with inverse halftoning

Fingerprints are the most popular and widely practiced biometric trait for human recognition and authentication. Due to the wide approval, reliable fingerprint template generation and secure saving of the generated templates are highly vital. Since fingers are permanently connected to the human body, loss of fingerprint data is irreversible. Cancelable fingerprint templates are used to overcome this problem. This paper introduces a novel cancelable fingerprint template generation mechanism using Visual Secret Sharing (VSS), data embedding, inverse halftoning, and super-resolution. During the fingerprint template generation, VSS shares with some hidden information are formulated as the secure cancelable template. Before authentication, the secret fingerprint image is reconstructed back from the VSS shares. The experimental results show that the proposed cancelable templates are simple, secure, and fulfill all the properties of the ideal cancelable templates, such as security, accuracy, non-invertibility, diversity, and revocability. The experimental analysis shows that the reconstructed fingerprint images are similar to the original fingerprints in terms of visual parameters and matching error rates.     

YOLO-based ROI selection for joint encryption and compression of medical images with reconstruction through super-resolution network

Medical images contain significant patient information, and this confidential data should not be accessed without proper authorisation. Concurrently, due to the high redundancy of image data, compression is necessary to minimise image size and efficiently utilise network resources. This paper presents an effective joint encryption and compression method for medical images that prevent critical data leakage while reducing redundancy. Initially, a powerful real-time object detection method, You Only Look Once v7, is employed to accurately and swiftly detect the region of interest (ROI) within the medical images. Subsequently, a joint three-dimensional chaotic map and Huffman encoding are applied to secure medical images without compromising the compression ratio or increasing the time cost. Lastly, a super-resolution network is established at the receiver end to better reconstruct the ROI image for precise diagnostic purposes. The comprehensive experimental analysis demonstrates that our method delivers high levels of security, compression, and visual quality performance on standard datasets used in smart healthcare applications, at a minimum. Furthermore, our approach outperforms other competitive state-of-the-art schemes when compared. We hope this study will inspire further research within the healthcare community.     

A super-resolution-based license plate recognition method for remote surveillance

With the continuous development of deep learning, neural networks have made great progress in license plate recognition (LPR). Nevertheless, there is still room to improve the performance of license plate recognition for low-resolution and relatively blurry images in remote surveillance scenarios. When it is difficult to enhance the recognition algorithm, we choose super-resolution (SR) to improve the quality of license plate images and thereby provide clearer input for the subsequent recognition stage. In this paper, we propose an automatic super-resolution license plate recognition (SRLPR) network which consists of four parts separately: license plate detection, character detection, single character super-resolution, and recognition. In the training stage, firstly, LP detection model needs to be trained alone and then its detection results will be used to successively train the three subsequent modules. During the test phase, for each input image, the network can get its LP number automatically. We also collect an applicable and challenging LPR dataset called SRLP, which is collected from real remote traffic surveillance. The experimental results demonstrate that our method achieves comprehensive quality of SR images and higher recognition accuracy compared with state-of-the-art methods. The SRLP dataset and the code for training and testing SRLPR network are available at https://pan.baidu.com/s/1vnhRa-c-dBj6jlfBZV5w4g.