Bayesian super-resolution of text in videowith a text-specific bimodal prior

To increase the range of sizes of video scene text recognizable by optical character recognition (OCR), we developed a Bayesian super-resolution algorithm that uses a text-specific bimodal prior. We evaluated the effectiveness of the bimodal prior, compared and in conjunction with a piecewise smoothness prior, visually and by measuring the accuracy of the OCR results on the variously super-resolved images. The bimodal prior improved the readability of 4- to 7-pixel-high scene text significantly better than bicubic interpolation and increased the accuracy of OCR results better than the piecewise smoothness prior.     

Steerable pyramid-based face hallucination

In this paper we propose a robust learning-based face hallucination algorithm, which predicts a high-resolution face image from an input low-resolution image. It can be utilized for many computer vision tasks, such as face recognition and face tracking. With the help of a database of other high-resolution face images, we use a steerable pyramid to extract multi-orientation and multi-scale information of local low-level facial features both from the input low-resolution face image and other high-resolution ones, and use a pyramid-like parent structure and local best match approach to estimate the best prior; then, this prior is incorporated into a Bayesian maximum a posterior (MAP) framework, and finally the high-resolution version is optimized by a steepest decent algorithm. The experimental results show that we can enhance a 24×32 face image into a 96×128 one while the visual effect is relatively good.     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.     

一种改善超分辨率图像重建中边缘质量的方法

超分辨率图像重建技术指通过融合多幅变形、模糊、有噪、频谱混叠的低分辨率降质图像来重建一幅高质量高分辨率图像. 凸集投影 (POCS) 算法是一种广泛使用的超分辨率图像重建方法. 本文提出了一种适用于 POCS 算法的改善高分辨率重建图像边缘质量的方法. 该方法将中心在边缘像素的点扩散函数 (PSF) 与一个指数型权值函数相乘, 使得修改的 PSF 系数沿着边缘正交的方向减小. 实验结果表明, 这样的修改有效地保持了边缘的特性, 明显地提高了重建图像的质量     

考虑亚像素配准误差的超分辨率图像复原

超分辨率图像复原作为第二代图像复原方向,已成为目前国际图像复原界的一个研究热点.一般来说,超分辨率图像复原是一个病态问题,可以结合图像的先验信息,使其成为良态的,这需要有效的规整化算法.但是,规整化参数的选择多数情况是通过经验确定的,且现有的一些计算规整化参数的方法又过于繁琐.本文讨论了亚像素配准误差引入的情况下噪声的统计模型,利用Miller规整的思想给出了简易可行的规整化参数计算方法.这种规整化参数计算方法能够自适应地根据配准误差和观测噪声局部调整由于配准误差导致的失真.仿真结果表明得到的规整化参数能使规整化算法有效收敛.     

基于两级字典与分频带字典的图像超分辨率算法

常规基于稀疏表示的超分辨率算法使用一级高低分辨字典重构图像, 恢复细节信息不充分. 本文利用两级字典恢复尽可能多的细节信息; 然后构造联合低频字典、中频字典、高频字典的分频带字典, 利用图像低频、中频、高频三者之间的预测关系, 恢复图像中的高频信息. 利用图像的非局部相似性, 将其与迭代反向投影算法相结合, 进行图像的后处理. 实验结果表明, 与其他几种基于学习的算法相比, 本算法无论是在峰值信噪比、结构相似性指标, 还是视觉效果上都有显著的提高.     

Experimental confirmation of super-resolution in coherent confocal scanning microscopy using optical masks

Experimental results are presented which demonstrate super-resolution in a coherent scanning microscope. The microscope has a special optical mask, a Fourier lens and detector pin-hole to carry out optical processing of the image. The form of the special mask was calculated using the theory of singular systems.     

用于细胞三维结构观察的现代显微影像仪器及相关技术

细胞三维结构的观察分析能提供更多细胞显微水平结构和功能的信息。现代显微影像仪器及相关的三维重建等技术是研究细胞三维结构的有力工具。本文概述多种可用于细胞三维结构观察的显微工具,简单介绍其原理、优势、最新技术动态及应用领域等。     

无源毫米波图像超分辨率处理方法

超分辨率处理是一个病态问题,有效地利用已知信息和约束条件是实现最佳处理的重要因素.论文以无源毫米波成像退化模型为基础,给出点扩展函数估计;通过引入典型目标和场景毫米波辐射特性,把POCS(凸集投影)和MAP(最大后验概率)结合起来,提出一种无源毫米波图像超分辨率处理方法.实验结果表明,该方法能够加快收敛速度,在平滑噪声的同时保护图像边缘和细节.     

基于高斯方向图的二维子阵级相干源超分辨测向

二维子阵级超分辨测向在相控阵雷达中具有重要应用.本文研究适用于相干源的子阵级ML估计方法.提出了子阵级ML估计的信号模型.利用子阵相位中心与增益来构造简化的阵列流形,使相控阵的校正成本与代价得到较大的降低.引入加权网络对子阵输出进行后处理,大大提高了阵列处理的灵活性.构造了高斯模式的子阵方向图.与直接简化的阵列流形方法相比,基于高斯方向图的简化阵列流形方法克服了其测向范围无法调整的局限性,且能更好抑制旁瓣源.仿真结果证实了所提出方法的有效性.