基于参数估计的降晰函数辨识及图像复原算法

成像系统的点扩展函数(PSF)以及观测噪声,在一般应用过程中是未知信息,因此,点扩展函数的辨识是一个具有挑战性的世界难题.为解决实际工作中遇到的在已知降晰类型情况下的降晰函数辨识和降晰图像复原问题,提出了基于参数估计的降晰函数辨识及降晰图像复原算法.首先,由初始猜测给定降晰函数参数的变化范围和参数的增量步长;然后,最小化降晰图像和由相应点扩展函数及降晰图像得到的实验观测图像的差的Frobenius范数,以确定点扩展函数的参数,进而确定降晰图像的点扩展函数并对降晰图像进行复原.应用基于Wiener滤波的频域循环边界算法对降晰图像进行复原.实验结果表明:在降晰图像信噪比较高的情况下,降晰函数的辨识结果是可靠和准确的,有较好的复原效果.     

基于各向异性规整化的总变分盲复原算法研究

针对大气湍流退化图像复原问题,提出了一种基于各向异性和非线性规整化的总变分盲复原新算法,该算法主要结合图像和湍流点扩展函数的一些性质采用基于各向异性的空间自适应规整化处理,建立了具有非线性和空间各向异性的规整化函数,使其在恢复目标图像和估计点扩展函数时能自适应地进行梯度平滑。最后,通过交替最小化方案来极小化代价函数和通过定点迭代策略将非线性方程进行线性化处理,快速地估计点扩展函数和恢复图像。在微机上对数字模拟和实际退化图像进行了一系列恢复实验,验证了算法的有效性和稳健性。     

Focused image recovery from two defocused images recorded withdifferent camera settings

Two new methods are presented for recovering the focused image of an object from only two blurred images recorded with different camera parameter settings. The camera parameters include lens position, focal length, and aperture diameter. First a blur parameter sigma is estimated using one of our proposed depth-from-defocus methods. Then one of the two blurred images is deconvolved to recover the focused image. The first method is based on a spatial domain convolution/deconvolution transform. This method requires only the knowledge of sigma of the camera's point spread function (PSF). It does not require information about the actual form of the camera's PSF. The second method, in contrast to the first, requires full knowledge of the form of the PSF. As part of the second method, we present a calibration procedure for estimating the camera's PSF for different values of the blur parameter sigma. In the second method, the focused image is obtained through deconvolution in the Fourier domain using a Wiener filter. For both methods, the results of experiments on actual defocused images recorded by a CCD camera are given. The first method requires much less computation than the second method. The first method gives satisfactory results for up to medium levels of blur and the second method gives good results for up to relatively high levels of blur.     

气动光学效应红外序列退化图像优化复原算法

从时间序列退化图像中依次连续取两帧图像来估计湍流瞬态点扩展函数,将约束优化原理应用在气动光学效应退化图像的复原过程中。针对气动光学效应湍流点扩展函数复杂多峰、随机多变等特性,在点扩展函数的估计过程中,采用保凸峰等优化策略,将点扩展函数离散值的计算转化为基于松弛迭代的最优化估计,通过极小化准则函数估计点扩展函数值,进而恢复退化图像。实验结果表明,本文方法复原效果好,速度较快。     

基于深度多级小波变换的图像盲去模糊算法

近年来卷积神经网络广泛应用于单幅图像去模糊问题,卷积神经网络的感受野大小、网络深度等会影响图像去模糊算法性能。为了增大感受野以提高图像去模糊算法的性能,该文提出一种基于深度多级小波变换的图像盲去模糊算法。将小波变换嵌入编-解码结构中,在增大感受野的同时加强图像特征的稀疏性。为在小波域重构高质量图像,该文利用多尺度扩张稠密块提取图像的多尺度信息,同时引入特征融合块以自适应地融合编-解码之间的特征。此外,由于小波域和空间域对图像信息的表示存在差异,为融合这些不同的特征表示,该文利用空间域重建模块在空间域进一步提高重构图像的质量。实验结果表明该文方法在结构相似度(SSIM)和峰值信噪比(PSNR)上具有更好的性能,而且在真实模糊图像上具有更好的视觉效果。

     

Spatial PSF Nonuniformity Effects in Airborne Pushbroom Imaging Spectrometry Data

Efficient and accurate imaging spectroscopy data processing asks for perfectly consistent (i.e., ideally uniform) data in both the spectral and spatial dimensions. However, real pushbroom-type imaging spectrometers are affected by various point spread function (PSF) nonuniformity artifacts. First, individual pixels or lines may be missing in the raw data due to bad pixels originating from the detector, readout errors, or even electronic failures. Second, so-called smile and keystone optical aberrations are inherent to imaging spectrometers. Appropriate resampling strategies are required for the preprocessing of such data if emphasis is put on spatial PSF uniformity. So far, nearest neighbor interpolations have been often recommended and used for resampling. This paper shall analyze the radiometric effects if linear interpolation is used to optimize the spatial PSF uniformity. For modeling interpolation effects, an extensive library of measured surface reflectance spectra as well as real imaging spectroscopy data over various land cover types are used. The real measurements are systematically replaced by interpolated values, and the deviation between original and resampled spectra is taken as a quality measure. The effects of nearest neighbor resampling and linear interpolation methods are compared. It is found that linear interpolation methods lead to average radiometric errors below 2% for the correction of spatial PSF nonuniformity in the subpixel domain, whereas the replacement of missing pixels leads to average errors in the range of 10%-20%     

Blind single-image super resolution based on compressive sensing

Blind super resolution is an interesting area in image processing that can restore high resolution (HR) image without requiring prior information of the volatile point spread function (PSF). In this paper, a novel framework is proposed for blind single-image super resolution (SISR) problem based on compressive sensing (CS) framework that is one of the first works that considers general PSFs. The fundamental idea in the proposed approach is to use sparsity on a known sparse transform domain as a powerful regularizer in both the image and blur domains. Therefore, a new cost function with respect to the unknown HR image patch and PSF kernel is presented and minimization is performed based on two subproblems that are modeled similar to that of CS. Simulation results demonstrate the effectiveness of the proposed algorithm that is competitive with methods that use multiple LR images to achieve a single HR image.     

Multi-spectral and hyperspectral image fusion using 3-D wavelet transform

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.     

Image-pair-based deblurring with spatially varying norms and noisy image updating

This paper presents a deblurring method that effectively restores fine textures and details, such as a tree’s leaves or regular patterns, and suppresses noises in flat regions using consecutively captured blurry and noisy images. To accomplish this, we used a method that combines noisy image updating with one iteration and fast deconvolution with spatially varying norms in a modified alternating minimization scheme. The captured noisy image is first denoised with a nonlocal means (NL-means) denoising method, and then fused with a deconvolved version of the captured blurred image on the frequency domain, to provide an initially restored image with less noise. Through a feedback loop, the captured noisy image is directly substituted with the initially restored image for one more NL-means denoising, which results in an upgraded noisy image with clearer outlines and less noise. Next, an alpha map that stores spatially varying norm values, which indicate local gradient priors in a maximum-a-posterior (MAP) estimation, is created based on texture likelihoods found by applying a texture detector to the initially restored image. The alpha map is used in a modified alternating minimization scheme with the pair of upgraded noisy images and a corresponding point spread function (PSF) to improve texture representation and suppress noises and ringing artifacts. Our results show that the proposed method effectively restores details and textures and alleviates noises in flat regions.     

高速运动目标的运动模糊图像复原研究

对单一图像进行运动模糊复原,存在模糊点扩散函数（PSF）难以估计以及图像反卷积的病态性问题。利用多个PSF具有联合可逆性的特点,针对运动目标观测,提出采用参数相同的多个成像设备共同对同一视场进行拍摄,来获取背景相同、曝光时间不同、目标模糊程度不同的观测图像;然后利用同一设备获取的序列图像进行目标的模糊PSF估计;并根据目标背景的运动模糊叠加特征,分别从观测图像中提取出完整的模糊目标图像;最后,对这些具有不同PSF的同一目标图像进行空间域迭代复原算式的联立求解。实验表明:该方法设计的目标获取装置对硬件条件要求较低,获取的图像更便于采用多点扩散函数联合进行图像复原,复原效果良好。     

Objective quality assessment of synthesized images by local variation measurement

Due to the rapid development of free-viewpoint television (FVT), Depth-Image-Based Rendering (DIBR) technology has been widely used to synthesize images of virtual view-points. However, the types of distortions in the synthesized images are different from those of natural images, such as discontinuity, flickering, stretching, etc. To measure the distortion occurred in the synthesized images, we propose a full-reference (FR) quality assessment method by local variation measurement consisting of three-modules. Firstly, since the distortion in the synthesized image mainly occurs in the region with high-frequency structure information, the Neutrosophic domain is employed to evaluate the degradation of local image structure. Secondly, by considering that the texture of the synthesized image might be damaged due to the warping of 2D image or the loss of information in the occlusion region, we evaluate the visual quality of local texture by using the features obtained from frequency domain. Thirdly, to measure the stretching distortion which is unique in the synthesized image, the visual quality of extracted stretching area is measured by entropy. Finally, a pooling operation is used to combine the quality scores of the three modules to obtain the final predicted quality score. Experimental results show that the performance of the proposed algorithm is competitive with state-of-the-art FR and no-reference image quality assessment metrics.     

Fusion of multifocus images by lattice structures

Image fusion methods based on multiscale transform (MST) suffer from high computational load due to the use of fast Fourier transforms (ffts) in the lowpass and highpass filtering steps. Lifting wavelet scheme which is based on second generation wavelets has been proposed as a solution to this issue. Lifting Wavelet Transform (LWT) is composed of split, prediction and update operations all implemented in the spatial domain using multiplications and additions, thus computation time is highly reduced. Since image fusion performance benefits from undecimated transform, it has later been extended to Stationary Lifting Wavelet Transform (SLWT). In this paper, we propose to use the lattice filter for the MST analysis step. Lattice filter is composed of analysis and synthesis parts where simultaneous lowpass and highpass operations are performed in spatial domain with the help of additions/multiplications and delay operations, in a recursive structure which increases robustness to noise. Since the original filter is designed for the undecimated case, we have developed undecimated lattice structures, and applied them to the fusion of multifocus images. Fusion results and evaluation metrics show that the proposed method has better performance especially with noisy images while having similar computational load with LSWT based fusion method.     

Enhancement of submicron optical lithography performance using phase-only pupil filters

We present a pupil filter design method based on iterative optimisation of the intensity point spread function (PSF) of a binary phase-only diffractive optical element, for use in a lens, diffractive element combination. Increase in the depth of focus and control over the sidelobe levels in the PSF is shown. The favourable performance is retained in simulated aerial images when the pupil filter is incorporated into a typical photolithography system.     

图像增强技术在指纹中的应用

描述了在空间域、频率域以及数学形态学上的图像增强技术，分析和讨论了空间域和频率域以及形态学三种方法在指纹上的应用。由于采用单一的图像增强技术不能达到令人满意的效果，有必要将多种方法结合使用。经过实验处理后得到的结果图像，表现出指纹中的突出特征，实验结果中有些特征是单一图像增强技术不能提取出来的。     

基于灰度直方图和谱聚类的文本图像二值化方法

在自动文本提取中,经定位获得的字符区域需二值化后方能有效识别,由于背景的复杂,常用的阈值化方法不能有效分割自然环境下的字符图像。该文提出了一种基于谱聚类的图像二值化方法,该方法利用规范化切痕(Normalized cut, Ncut)作为谱聚类测度,结合灰度直方图计算相似性矩阵,并通过实验确定最佳的直方图等级数,与通常基于像素级相似矩阵相比,算法的空间复杂度和计算复杂性都大为降低。实验结果表明,针对自然场景下的字符图像,该文方法的二值化结果优于常用的阈值分割结果。     

Disparity-based space-variant image deblurring

Obtaining a good-quality image requires exposure to light for an appropriate amount of time. If there is camera or object motion during the exposure time, the image is blurred. To remove the blur, some recent image deblurring methods effectively estimate a point spread function (PSF) by acquiring a noisy image additionally, and restore a clear latent image with the PSF. Since the groundtruth PSF varies with the location, a blockwise approach for PSF estimation has been proposed. However, the block to estimate a PSF is a straightly demarcated rectangle which is generally different from the shape of an actual region where the PSF can be properly assumed constant. We utilize the fact that a PSF is substantially related to the local disparity between two views. This paper presents a disparity-based method of space-variant image deblurring which employs disparity information in image segmentation, and estimates a PSF, and restores a latent image for each region. The segmentation method firstly over-segments a blurred image into sufficiently many regions based on color, and then merges adjacent regions with similar disparities. Experimental results show the effectiveness of the proposed method.     

基于二维最大边缘准则和DCT的人脸识别方法

提出了一种基于离散余弦变换(DCT)和二维最大边缘准则(2DMMC)的2DDM特征提取算法,证明了2DMMC可以直接应用于DCT域,利用欧氏距离测度进行分类的结果与在空域中进行得到的结果完全相同。2DMMC方法可直接应用于基于DCT压缩的JPEG格式的图像。在ORL和Yale人脸数据库上的实验结果表明,在空域2DMMC的识别率高于2DPCA和2DLDA,2DDM的识别率又高于2DMMC,而且2DDM的耗时要低于2DMMC。     

Generalized IHS‐Based Satellite Imagery Fusion Using Spectral Response Functions

Image fusion is a technical method to integrate the spatial details of the high‐resolution panchromatic (HRP) image and the spectral information of low‐resolution multispectral (LRM) images to produce high‐resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high‐quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity‐hue‐saturation and wavelet‐based methods.     

采用边缘Gibbs现象抑制的红外图像POCS重构算法

在研究和分析凸集投影(Projection Onto Convex Sets,POCS)法的基础上,提出了一种改进的图像超分辨率重构算法.该算法充分利用空域确定模型,通过平滑降噪处理,经运动估计进行配准;突出数字图像细节信息的同时,有针对性地修改点扩散函数(Point Spread Function,PSF)取值,通过有效抑制边缘Gibbs现象获得最佳质量的高分辨率重构图像.对重构后的红外图像质量进行了定量评价.结果表明,图像质量取得了预期的效果.该技术在红外目标识别与跟踪、红外侦察与反侦察、舰船红外目标特性研究、高清数字图像处理、旧视频翻录和生物信息提取与识别等方面具有重要的应用价值.     

动目标多视角观测图像的三维去模糊方法

当目标在多视角观测成像系统中有相对运动时,所获取的多观测点图像是模糊的且各视角图像的模糊是不一样的,模糊核长度和方向都不同。针对这一问题,提出了多视角观测图像的三维去模糊方法。现有的图像去模糊方法主要是对单视角观测图像进行二维去模糊的,没有考虑目标多视角观测图像的模糊核之间的对应关系。文中从三维空间到二维观测面的映射关系出发,建立多视角观测图像的模糊路径之间的对应关系。先采用单观测图像去模糊的方法获取两视角观测图像的模糊核,并对模糊核进行精确化处理,得到单像素点宽的模糊核路径。再通过多视角观测图像模糊核路径之间的对应关系,估计其它观测图像的模糊核路径。最后,对多视角观测图像进行统一去模糊,并对去模糊后的多视角观测图像进行三维重建。实验结果表明,文中方法能较好地去除目标多视角观测图像的三维模糊,提高了目标的三维重建质量。