LED视频显示屏的图像质量和视距

运用数字图像处理的基本理论。分析了LED显示屏及其重现的视频图像的空间频谱特性，说明了图像分辨率、锐度、混叠伪像与显示屏参数（包括点间距和填充系数）的关系。本文还着重讨论了LED视频显示屏的视距问题，得出相应的结论。     

基于自适应牛顿算法的LED视频显示系统的研制

LED显示屏已被广泛应用于各种场合,通常情况下,需要对视频图像进行缩放处理,从而符合LED显示屏的物理分辨率.针对传统LED插值缩放算法在插值后导致图像边缘模糊的现象,提出了一种自适应牛顿播值算法,通过在插值之前进行像素相关度的自适应判断,可以较好地保留图像的高频信息,消除图像边缘模糊的问题.同时,给出了基于自适应牛顿算法的LED视频显示系统的FPGA实现,系统最终可以实现较好的显示效果.     

基于平滑滤波的虚拟显示模型及控制方法

针对新型虚拟LED显示屏提出一种基于平滑滤波器的虚拟显示模型和控制方法,该方法与传统方法相比能在降低数据带宽的条件下提高虚拟显示的灰度等级。分析了户外发光二极管显示屏(LED)的显示原理,指出提高LED显示器分辨率和灰度是提升其显示效果最直接的方法。介绍了传统虚拟显示的实现及控制原理和存在的问题,然后针对目前的虚拟LED显示屏提出一种虚拟显示的建模及控制方法,再用模拟和实验验证该方法的可行性及效果,最后与传统的虚拟显示建模及控制方法做对比。实验结果表明,使用本文提出的建模和控制方法呈现的图像清晰,图像细节不流失,准确地还原了图像原来真实的情况。本文提出的虚拟显示建模和控制方法能够准确还原图像信息,可以用于新型的虚拟LED显示屏。     

天津光电星球阳光显示技术有限公司LED全彩显示屏简介

LED全彩色显示屏是集微电子技术、光学技术、计算机技术、信息处理技术于一体的大型显示系统。产品采用日本及美国进口高亮度发光二极管或三合一表贴发光二极管,灰度等级达到1,024级,图像分辨率最高可达1,280×1,024。其驱动控制系统采用恒流驱动芯片使LED显示屏的控制更良好、     

一种有效的图像复原方法

图像超分辨率复原技术是由一序列低分辨率变形图像估计一幅或多幅较高分辨率的非变形图像,同时还能够消除加性噪声以及由有限检测器尺寸和光学产生的模糊,是图像融合领域中的一个重要分支。先把高分辨率图像变换成低分辨率图像,然后对低分辨率图像进行运动估计和运动补偿计算,最后再把低分辨率图像映射灰高分辨率图像。     

LED显示屏视频图像分解力提升技术综述

本文分析了影响LED显示屏视频图像分解力的两大环节:(1)视频前端处理技术;(2)显示终端像素分辨率,并阐述了其特点。尤其是对目前业内各种关于LED显示终端分辨率提升技术混乱的提法给出了明确的定义,供大家探讨,希望能够逐步统一定义,形成标准,从而规范市场。     

联合运动估计与基于模式的超分辨率图像重构

超分辨率图像重构是利用关于同一场景的多帧低分辨率图像重构出一幅具有更高分辨率图像的过程.已有的超分辨率图像重构算法对于人工模拟所得到的低分辨率图像序列具有很好的效果,但对于拍摄到的真实低分辨率图像序列而言,重构后的图像往往比较模糊,有时甚至仍然无法分辨.为此,本文提出了一个联合运动估计与基于模式的超分辨率图像重构算法.实验结果表明,该算法能够得到优于常规算法的高分辨率图像.     

遥感图像超分辨率复原算法的仿真实现

超分辨率图像复原是指使用一组低分辨率图像进行处理,得到一幅高分辨率图像。分析超分辨率处理算法并将其应用于遥感图像分辨率增强领域,提出一种用Matlab对遥感图像进行超分辨率处理的仿真方法,仿真结合POC原理将一组低分辨率遥感图像进行分辨率增强处理,结果表明超分辨率处理技术有效提高了遥感图像的分辨率,图像中目标更易识别。     

机械扫描式平面LED显示屏的设计

介绍了机械扫描式平面LED显示屏的原理与设计方法,针对该LED显示屏像素分布不均匀的问题,提出了旋转显示屏图像均匀化算法.仿真结果表明,机械扫描式平面LED显示屏仅用一列LED即可达到与普通点阵式显示屏相似的显示效果,大幅减少了LED像素的使用,降低了显示屏的成本.     

基于双线性插值算法的低分辨率传感器标定方法

针对无扫描激光三维传感器成像分辨率较低、标定精度不高的问题,提出了一种基于双线性插值的低分辨率传感器标定方法。首先运用双线性插值算法,对低分辨率传感器所成图像进行升采样以提高图像分辨率,然后利用基于OpenCV标定算法对其进行标定,最后将标定结果与传统方法标定结果进行分析比较。实验结果表明,该方法能够将低分辨率传感器参数的标定误差缩小近1/2。运用双线性插值算法提高图像分辨率,可以提高对低分辨率传感器的标定精度。     

基于稀疏表征的图像超分辨算法研究

超分辨率图像恢复的目的是由低分辨率图像得到高分辨率图像,通常需要多幅或者一系列连续低分辨率图像．在有限的条件下很难得到。针对单幅图像超分辨问题,结合当前比较先进的稀疏表征方法,利用训练集图像的先验信息．对单幅图像进行超分辨率恢复。结合当前先进的基于稀疏表征的超分辨算法,采用误差反投影方法,提出一种改进的算法．对超分辨率...     

Singular value decomposition based fusion for super-resolution image reconstruction

In this paper, we address a super-resolution problem of generating a high-resolution image from low-resolution images. The proposed super-resolution method consists of three steps: image registration, singular value decomposition (SVD)-based image fusion and interpolation. The contribution of this work is two-fold. First we customize an image registration approach using Scale Invariant Feature Transform (SIFT), Belief Propagation and Random Sampling Consensus (RANSAC) for super-resolution. Second, we propose SVD-based fusion to integrate the important features from the low-resolution images. The proposed image registration and fusion steps effectively maintain the important features and greatly improve the super-resolution results. Results, for a variety of image examples, show that the proposed method successfully generates high-resolution images from low-resolution images.     

Hyperspectral image quality based on convolutional network of multi-scale depth

Hyperspectral imagery has been widely used in military and civilian research fields such as crop yield estimation, mineral exploration, and military target detection. However, for the limited imaging equipment and the complex imaging environment of hyperspectral images, the spatial resolution of hyperspectral images is still relatively low, which limits the application of hyperspectral images. So, studying the data characteristics of hyperspectral images deeply and improving the spatial resolution of hyperspectral images is an important prerequisite for accurate interpretation and wide application of hyperspectral images. The purpose of this paper is to deal with super-resolution of the hyperspectral image quickly and accurately, and maintain the spectral characteristics of the hyperspectral image, makes the spectral separability of the substrate in the original image remains unchanged after super-resolution processing. This paper first learns the mapping relationship between the spectral difference of low-resolution hyperspectral image and the spectral difference of the corresponding high-resolution hyperspectral image based on multiple scale convolutional neural network, Thus, apply this mapping relationship to the input low-resolution hyperspectral image generally, getting the corresponding high resolution spectral difference. Constrained space by using the image of reconstructed spectral difference, this requires the low-resolution hyperspectral image generated by the reconstructed image is to be close to the input low-resolution hyperspectral image in space, so that the whole process becomes a closed circulation system where the low-resolution hyperspectral image generation of high-resolution hyperspectral images, then back to low-resolution hyperspectral images. This innovative design further enhances the super-resolution performance of the algorithm. The experimental results show that the hyperspectral image super-resolution method based on convolutional neural network improves the input image spatial information, and the super-resolution performance of the model is above 90%, which can maintain the spectral information well.     

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.     

Integrating the Missing Information Estimation into Multi-frame Super-Resolution

Multi-frame super-resolution image reconstruction aims to restore a high-resolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback–Leibler divergence. In this way, the missing information is estimated simultaneously with the high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.     

Polyphase back-projection filtering for image resolution enhancement

The method for reconstruction and restoration of super-resolution images from sets of low-resolution images presented is an extension of the algorithm proposed by Irani and Peleg (1991). After estimating the projective transformation parameters between the image sequence frames, the observed data are transformed into a sequence with only quantised sub-pixel translations. The super-resolution reconstruction is an iterative process, in which a high-resolution image is initialised and iteratively improved. The improvement is achieved by back-projecting the errors between the translated low-resolution images and the respective images obtained by simulating the imaging system. The imaging system's point-spread function (PSF) and the back-projection function are first estimated with a resolution higher than that of the super-resolution image. The two functions are then decimated so that two banks of polyphase filters are obtained. The use of the polyphase filters allows exploitation of the input data without any smoothing and/or interpolation operations. The presented experimental results show that the resolution improvement is better than the results obtained with Irani and Peleg's algorithm.     

POCS超分辨率图像重构的快速算法

超分辨率图像重构是将多帧低分辨率图像重构成一幅高分辨率图像的过程。由于其求解是一大型病态求逆问题，计算量随着放大倍数的增加而急剧上升，如何降低计算复杂度是超分辨率成像所面临的一个急需解决的课题。提出了一个基于PoCs的高分辨率图像重构的快速算法。其原理是利用各低分辨率图像之间位移的关系将所有的低分辨率图像进行重组，然后对每个组进行PoCs超分辨图象重构。实验结果表明。该快速算法较大地提高了超分辨图像重构的速度。     

L1范数的图像超分辨率重建改进算法

介绍了超分辨率图像重建的数学模型和基于L1范数的超分辨率重建算法。针对在所观察到的低分辨率图像不足情况下的超分辨率重建,在L1范数重建算法框架下,提出了一种新的代价方程,在其中增加了关于丢失的低分辨率观察信息的保真度项和正则化项。该方法同时对高分辨率图像和丢失的观察信息进行迭代估计,并利用交替最小方法求解。实验结果表明,在获取低分辨率图像较少的情况下,提出的算法能够有效地改进重建的结果。     

Accurate image registration for MAP image super-resolution

The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.     

面向图像超分辨率的紧凑型多径卷积神经网络算法研究

为改善单帧图像分辨率退化问题,减少网络参数,本文提出一种基于紧凑型多径结构卷积神经网络的图像超分辨率重构算法。本文算法采用多径结构模型充分使用低分辨率图像信息,并利用残差学习策略学习低分辨率和高分辨率图像间残差信息以重建高分辨率图像。当卷积核数量有限时,含有ReLU的网络重构性能表现不佳,因此引入最大特征图激活函数,增强网络泛化能力,使网络结构更加紧凑,以捕捉具有竞争性特征,完成图像超分辨率重构。实验结果表明,本文方法具有良好的重构能力,图像清晰度和边缘锐度明显提高,在客观评价和主观视觉效果方面优于当前主流的超分辨率重构方法。为便携式高性能超分辨率重构奠定理论基础。