A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

High-resolution video transmission requires a substantial amount of bandwidth.In this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution enhancement.Our method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data transmission.To validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical applications.The proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement tasks.This unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom dataset.The implications of this research extend to optimizing low-bitrate video streaming scenarios.By selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth conditions.Empirical results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual quality.This work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited environments.The innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

基于深度SR模型的加密数字图像压缩与重构

针对图像压缩后降低存储空间的同时也降低图像分辨率的问题,提出一种基于深度超分辨率(SR)模型的加密数字图像压缩与重构方法。先对加密数字图像进行分割,然后针对分割后的图像子块进行编码压缩处理,并将经典的SR重构方法(稀疏编码法)与深度学习(卷积神经网络)进行结合,构建一种深度SR模型,并利用模型对图像进行压缩和解压,最后对解密后的数字图像进行重构。结果表明:图像压缩后较压缩前占据存储空间降低,压缩效果有所改善,经过深度SR模型重构后的数字图像分辨率相对更高,且峰值信噪比更高。

Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

High-resolution medical images have important medical value,but are difficult to obtain directly.Limited by hardware equipment and patient’s physical condition,the resolution of directly acquired medical images is often not high.Therefore,many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images.However,current super-resolution algorithms only work on a single scale,and multiple networks need to be trained when super-resolution images of different scales are needed.This definitely raises the cost of acquiring high-resolution medical images.Thus,we propose a multi-scale superresolution algorithm using meta-learning.The algorithm combines a metalearning approach with an enhanced depth of residual super-resolution network to design a meta-upscale module.The meta-upscale module utilizes the weight prediction property of meta-learning and is able to perform the super-resolution task of medical images at any scale.Meanwhile,we design a non-integer mapping relation for super-resolution,which allows the network to be trained under non-integer magnification requirements.Compared to the state-of-the-art single-image super-resolution algorithm on computed tomography images of the pelvic region.The meta-learning multiscale superresolution algorithm obtained a surpassing of about 2%at a smaller model volume.Testing on different parts proves the high generalizability of our algorithm.Multi-scale super-resolution algorithms using meta-learning can compensate for hardware device defects and reduce secondary harm to patients while obtaining high-resolution medical images.It can be of great use in imaging related fields.

Deep Learned Singular Residual Network for Super Resolution Reconstruction

Single image super resolution(SISR)techniques produce images of high resolution(HR)as output from input images of low resolution(LR).Motivated by the effectiveness of deep learning methods,we provide a framework based on deep learning to achieve super resolution(SR)by utilizing deep singular-residual neural network(DSRNN)in training phase.Residuals are obtained from the difference between HR and LR images to generate LR-residual example pairs.Singular value decomposition(SVD)is applied to each LR-residual image pair to decompose into subbands of low and high frequency components.Later,DSRNN is trained on these subbands through input and output channels by optimizing the weights and biases of the network.With fewer layers in DSRNN,the influence of exploding gradients is reduced.This speeds up the learning process and also improves accuracy by using skip connections.The trained DSRNN parameters yield residuals to recover the HR subbands in the testing phase.Experimental analysis shows that the proposed method results in superior performance to existingmethods in terms of subjective quality.Extensive testing results on popular benchmark datasets such as set5,set14,and urban100 for a scaling factor of 4 show the effectiveness of the proposed method across different qualitative evaluation metrics.

太赫兹MIMO系统中基于SRCGAN的空时频信道估计方案

为了能有效利用THz-MIMO系统的多维信道特性,提出一种基于SRCGAN的THz-MIMO系统信道估计方案。在该方案中,由预估计模块获得的初始空时域信道响应矩阵被视作一张二维的低分辨率图像,利用SRCGAN网络提取太赫兹信道的空时特性进行空时域信道补全获得完整的信道信息,然后相邻子载波之间的频率相关性作为SRGAN提供的条件信息提升信道估计精度。为了增强SRCGAN网络对时变信道预测的鲁棒性,在线上估计阶段,基于最大均方误差准则采用梯度下降算法对输入的预估计信道信息矩阵进行迭代更新。仿真结果证明了基于SRCGAN的空时频信道估计方案性能的优越性,以及利用信道“空时频”的相关性提升估计精度的有效性。

A Novel AlphaSRGAN for Underwater Image Super Resolution

Obtaining clear images of underwater scenes with descriptive details is an arduous task.Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors.Consequently,a need for a system that produces clear images for underwater image study has been necessitated.To overcome problems in resolution and to make better use of the Super-Resolution(SR)method,this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network(AlphaGAN)model,named Alpha Super Resolution Generative Adversarial Network(AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details.Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator.After the images are processed by the generator network,they are passed through an adversarial method for training models.The dataset used in this paper to learn Single Image Super Resolution(SISR)is the USR 248 dataset.Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality.Appraisal of images is done with reference to factors like local style information,global content and color.The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images—high(640×480)and low(80×60,160×120,and 320×240).Paired instances of different sizes—2×,4×and 8×—are also present in the dataset.Parameters like Mean Opinion Score(MOS),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM)and Underwater Image Quality Measure(UIQM)scores have been compared to validate the improved efficiency of our model when compared to existing works.

基于Rs-BasicVSR的煤机装备视频超分辨率算法

煤矿井下煤机装备搭载的摄像仪传输的视频信息存在信号干扰,分辨率低,细节缺失等问题,对后续的视频智能分析有着极大挑战。针对该问题,提出一种基于Rs-Basic VSR的煤机装备视频超分辨率算法。首先,在光流对齐部分改用RAFT网络,通过对图像特征建立相关空间,并根据特征信息进行搜索,提升光流计算精度;接着对网络结构进行裁剪,减少卷积层个数。实验结果表明,该算法在平均PSNR上可达31.83 dB,SSIM上达到了0.8919,平均推理时间为60 ms,模型参数量为5.9×10~6,主观视觉效果和客观指标上可以满足煤矿井下视频处理需求。

基于Real-ESRGAN和改进YOLOv8n的输电线路绝缘子故障检测

为解决无人机在输电线路巡检时遇到的绝缘子故障难以检测的问题,提出一种绝缘子故障检测新方法。该方法结合了真实世界增强超分辨率生成对抗网络(Real-ESRGAN)和改进的YOLOv8n。首先,利用Real-ESRGAN对数据集进行超分辨率重构,优化数据集质量,有效减少复杂背景的干扰;然后利用高效视觉变压器框架替换YOLOv8的主干,加强模型的特征提取能力,同时使模型在推理阶段有更快的处理速度;再对YOLOv8的检测头进行轻量化处理,进一步加速模型推理。试验结果显示,该方法的均值平均精度达86.7%,证明了其在复杂背景下的卓越目标检测性能。通过分析热力图,展示了该算法与传统YOLOv8在关注区域上的差异,从而揭示了模型的内部工作机理。

Super-Resolution Stress Imaging for Terahertz-Elastic Based on SRCNN

Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.

基于Real-ESRGAN的岩石CT图像超分辨率重建

图像采集设备和地质环境等因素导致岩石CT图像分辨率低、细节不清晰,而现有图像超分辨率重建方法在表征内部高密度矿物质颗粒和孔裂隙时容易丢失细节。针对上述问题,采用改进的增强型超分辨率生成对抗网络(Real-ESRGAN)对岩石CT图像进行超分辨率重建。选取山西晋城无烟煤矿业集团有限责任公司赵庄煤矿15号煤层底板的砂岩为研究对象,研究不同图像放大倍数下Real-ESRGAN的重建性能,并将其与超分辨率卷积神经网络(SRCNN)、超分辨率生成对抗网络(SRGAN)、增强型超分辨率生成对抗网络(ESRGAN)、增强的深度超分辨率网络(EDSR)等算法进行对比。试验结果表明:(1)使用Real-ESRGAN重建的高分辨率图像在视觉效果上比原始CT图像更清晰,重建图像中裂隙轮廓和高密度矿物质颗粒更加突出,图像可视性得到了极大提高。(2)在客观评估方面,Real-ESRGAN算法在2倍超分辨率重建后图像的峰值信噪比(PSNR)高达36.880 dB,结构相似性(SSIM)达0.933。但随着放大倍数的增加,6倍超分辨率重建图像上的孔隙出现模糊,PSNR降至32.781 dB,SSIM为0.896。(3)Real-ESRGAN重建超分辨图像的孔隙率和喉道长度分布占比与原始CT图像相比非常接近,保留了岩石重要的细观结构信息。

LaBr_(3):Ce,Sr闪烁晶体的生长及性能研究

采用自发成核坩埚下降法生长了直径25 mm的铈、锶共掺溴化镧(LaBr_(3)∶5%Ce,x%Sr,简称LaBr_(3)∶Ce,Sr,其中x=0.1、0.3、0.5,摩尔分数)闪烁晶体,测试对比了晶体的X射线激发发射光谱、透过光谱和脉冲高度谱等。结果表明,不同Sr^(2+)掺杂浓度的LaBr_(3)∶Ce,Sr晶体在X射线激发下的发射光谱波形基本一致,但相比未掺杂Sr^(2+)的样品,发射峰的峰位发生了明显的红移,随着Sr^(2+)掺杂浓度的增大,发射峰红移程度增大。不同Sr^(2+)掺杂浓度的LaBr_(3)∶Ce,Sr晶体在350~800 nm不存在明显的吸收峰,0.3%和0.5%Sr^(2+)掺杂晶体的透过率有所降低。随着Sr^(2+)掺杂浓度的增大,能量分辨率逐步提高,Sr^(2+)掺杂浓度为0.5%时,LaBr_(3)∶Ce,Sr晶体的能量分辨率最高,达2.99%@662 keV。对尺寸φ25 mm×25 mm的LaBr3∶Ce,0.5%Sr晶体进行了防潮封装,所得晶体封装件的能量分辨率为2.93%@662 keV。

Super-resolution fluorescence polarization microscopy

Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological macromolecules.Several recent super resolution techniques have beenapplied to fluorescence polarization microscopy,achieving dipole measurement at nanoscale.In this review,we summarize both difraction limited and super resolution fluorescence polari-zation microscopy techniques,as well as their applications in biological imaging.

Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm

One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution(SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming.In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency(NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature(RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms.

Arbitrary Scale Super Resolution Network for Satellite Imagery

Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.

Data Matching of Solar Images Super-Resolution Based on Deep Learning

The images captured by different observation station have different resolutions.The Helioseismic and Magnetic Imager(HMI:a part of the NASA Solar Dynamics Observatory SDO)has low-precision but wide coverage.And the Goode Solar Telescope(GST,formerly known as the New Solar Telescope)at Big Bear Solar Observatory(BBSO)solar images has high precision but small coverage.The super-resolution can make the captured images become clearer,so it is wildly used in solar image processing.The traditional super-resolution methods,such as interpolation,often use single image’s feature to improve the image’s quality.The methods based on deep learning-based super-resolution image reconstruction algorithms have better quality,but small-scale features often become ambiguous.To solve this problem,a transitional amplification network structure is proposed.The network can use the two types images relationship to make the images clear.By adding a transition image with almost no difference between the source image and the target image,the transitional amplification training procedure includes three parts:transition image acquisition,transition network training with source images and transition images,and amplification network training with transition images and target images.In addition,the traditional evaluation indicators based on structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)calculate the difference in pixel values and perform poorly in cross-type image reconstruction.The method based on feature matching can effectively evaluate the similarity and clarity of features.The experimental results show that the quality index of the reconstructed image is consistent with the visual effect.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

A Regularized Super Resolution Algorithm for Generalized Gaussian Noise

In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.

Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

The smart grid is an evolving critical infrastructure,which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services.To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid,state estimation,which serves as a basic tool for understanding the true states of a smart grid,should be performed with high frequency.More complete system state data are needed to support high-frequency state estimation.The data completeness problem for smart grid state estimation is therefore studied in this paper.The problem of improving data completeness by recovering highfrequency data from low-frequency data is formulated as a super resolution perception(SRP)problem in this paper.A novel machine-learning-based SRP approach is thereafter proposed.The proposed method,namely the Super Resolution Perception Net for State Estimation(SRPNSE),consists of three steps:feature extraction,information completion,and data reconstruction.Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

OBJECT-BASED SUPER RESOLUTION FOR INTELLIGENT VISUAL SURVEILLANCE VIDEO

Construction of high resolution images from low resolution sequences is often im- portant in surveillance applications. In this letter, an affine based multi-scale block-matching image registration algorithm is first proposed. The images to be registered are divided into overlapped blocks of different size according to its motions. The Least Square (LS) image reg- istration algorithm is extended to match the blocks. Then an object based Super Resolution (SR) scheme is designed, the Maximum A Priori (MAP) super resolution algorithm is extended to enhance the resolution of the interest objects. Experimental results show that the proposed multi-scale registration method provides more accurate registration between frames. Further more, the object based super resolution scheme shows an enhanced performance compared with the traditional MAP method.

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.