A multiresolution‐based method for the determination of the relative resolution between images: First application to remote sensing and medical images

Spatial resolution is a key parameter of all kind of images. This is of particular importance in fields as, for example, medicine or remote sensing. The nominal resolution of a positron emission tomography (PET) or nuclear magnetic resonance (NMR) scanners are directly related to the size, number, and position of the detectors in the scanner ring. Also, the nominal spatial resolution of the remote sensing satellites is a well‐known characteristic because it is directly related to the area in ground that represents a pixel in the detector. Nevertheless, in practice, the actual resolution of a medical scanner image or of an image obtained from a satellite is difficult to know precisely because it depends of many other factors. However, if we have two or more images of the same region of interest, obtained using similar or different instruments, it is possible to compare the relative resolution between them. In this paper we propose a wavelet‐decomposition‐based method for the determination of the relative resolution between two images of the same area. The method can be applied, in principle, to any kind of images. As example, we applied the method to pairs of remote sensing and medical images. In the case of remote sensing, we computed the relative resolution between SPOT‐3, LANDSAT‐5 and LANDSAT‐7 panchromatic and multispectral images taken under similar as well as under very different conditions. In the case of medical imaging, we computed the relative resolution between a pair of simultaneously obtained PET and NMR images of the same object. On the other hand, if we know the true absolute resolution of one of the images of the pair, we can compute the resolution of the other. Thus, in the last part of this paper, we describe a spatial calibrator that we have designed and constructed to help compute the absolute resolution of a single remotely sensed image, presenting an example of its use. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 225–235, 2005     

Face recognition performance with superresolution

With the prevalence of surveillance systems, face recognition is crucial to aiding the law enforcement community and homeland security in identifying suspects and suspicious individuals on watch lists. However, face recognition performance is severely affected by the low face resolution of individuals in typical surveillance footage, oftentimes due to the distance of individuals from the cameras as well as the small pixel count of low-cost surveillance systems. Superresolution image reconstruction has the potential to improve face recognition performance by using a sequence of low-resolution images of an individual's face in the same pose to reconstruct a more detailed high-resolution facial image. This work conducts an extensive performance evaluation of superresolution for a face recognition algorithm using a methodology and experimental setup consistent with real world settings at multiple subject-to-camera distances. Results show that superresolution image reconstruction improves face recognition performance considerably at the examined midrange and close range.     

A novel brain image segmentation using intuitionistic fuzzy C means algorithm

A process of splitting the image into pixel bands is the image segmentation. As medical imaging contain uncertainties, there are difficulties in classification of images into homogeneous regions. There is a need for segmentation algorithm for removing the noise from the medical image segmentation. The very popular algorithm is Fuzzy C‐Means (FCM) algorithm used for image segmentation. Fuzzy sets, rough sets, and the combination of fuzzy and rough sets play a prominent role in formalizing uncertainty, vagueness, and incompleteness in diagnosis. But it will use intensity values only which will be highly sensitive to noise. In this article, an Intuitionistic FCM (IFCM) algorithm is presented for clustering. Intuitionistic fuzzy (IF) sets are generalized sets and their elements are characterized by a membership value as well as nonmembership value. This IFCM has an uncertainty parameter which is called hesitation degree and a new objective function is integrated in the standard FCM based on IF entropy. The IFCM will provide better performance than FCM for image segmentation.     

Image reconstruction with a shift‐variant filtration in circular cone‐beam CT

It is well known that cone‐beam data acquired with a circular orbit are insufficient for exact image reconstruction. Despite this, because a cone‐beam scanning configuration with a circular orbit is easy to implement in practice, it has been widely employed for data acquisition in, e.g., micro‐CT and CT imaging in radiation therapy. The algorithm developed by Feldkamp, Davis, and Kress (FDK) and its modifications, such as the Tent–FDK (T‐FDK) algorithm, have been used for image reconstruction from circular cone‐beam data. In this work, we present an algorithm with spatially shift‐variant filtration for image reconstruction in circular cone‐beam CT. We performed computer‐simulation studies to compare the proposed and existing algorithms. Numerical results in these studies demonstrated that the proposed algorithm has resolution properties comparable to, and noise properties better than, the FDK algorithm. As compared to the T‐FDK algorithm, our proposed algorithm reconstructs images with an improved in‐plane spatial resolution. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 14, 213–221, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20026     

Small-kernel superresolution methods for microscanning imaging systems

Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented. Microscanning creates multiple low-resolution images with slightly different sample-scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end, continuous-discrete-continuous microscanning imaging system model. Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.     

Superresolution image reconstruction from a sequence of aliased imagery

We present a superresolution image reconstruction from a sequence of aliased imagery. The subpixel shifts (displacement) among the images are unknown due to the uncontrolled natural jitter of the imager. A correlation method is utilized to estimate subpixel shifts between each low-resolution aliased image with respect to a reference image. An error-energy reduction algorithm is derived to reconstruct the high-resolution alias-free output image. The main feature of this proposed error-energy reduction algorithm is that we treat the spatial samples from low-resolution images that possess unknown and irregular (uncontrolled) subpixel shifts as a set of constraints to populate an oversampled (sampled above the desired output bandwidth) processing array. The estimated subpixel locations of these samples and their values constitute a spatial domain constraint. Furthermore, the bandwidth of the alias-free image (or the sensor imposed bandwidth) is the criterion used as a spatial frequency domain constraint on the oversampled processing array. The results of testing the proposed algorithm on the simulated low- resolution forward-looking infrared (FLIR) images, real-world FLIR images, and visible images are provided. A comparison of the proposed algorithm with a standard interpolation algorithm for processing the simulated low-resolution FLIR images is also provided.     

Low‐cost solution to on‐line color filter array demosaicking

The article introduces a low‐cost algorithm for improving the demosaicking process in the texture areas such as one‐pixel patterns. The algorithm first detects difficult texture regions. After the detection process is completed, the algorithm demosaicks the texture areas using special demosaicking operations whereas non‐texture regions are restored using some of the existing demosaicking approaches. In this way, the quality of the texture areas in demosaicked images can be improved up to 70% while only little increasing the computational complexity of the original demosaicking solution. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 232–243, 2007     

复小波的亚像元图像重建及去噪方法

通过对亚像元理论的分析,对两帧错半个像元的遥感图像进行复小波插值,引入基于层间的小波自适应阈值方法去除噪声,并通过重构得到更高分辨率的遥感图像,同时,算法对遥感图像的复原效果好于常用的方法。     

Superresolution image reconstruction from blurred observations by multisensors

Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data‐fitting term (DFT), the regularization term for the reconstructed image, and the observed low‐resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H¹‐norm or total‐variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 153–160, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10053     

Mathematical extrapolation of image spectrum for constraint-set design and set-theoretic superresolution

Several powerful iterative algorithms are being developed for the restoration and superresolution of diffraction-limited imagery data by use of diverse mathematical techniques. Notwithstanding the mathematical sophistication of the approaches used in their development and the potential for resolution enhancement possible with their implementation, the spectrum extrapolation that is central to superresolution comes in these algorithms only as a by-product and needs to be checked only after the completion of the processing steps to ensure that an expansion of the image bandwidth has indeed occurred. To overcome this limitation, a new approach of mathematically extrapolating the image spectrum and employing it to design constraint sets for implementing set-theoretic estimation procedures is described. Performance evaluation of a specific projection-onto-convex-sets algorithm by using this approach for the restoration and superresolution of degraded images is outlined. The primary goal of the method presented is to expand the power spectrum of the input image beyond the range of the sensor that captured the image.     

A Magnifying Glass for Virtual Imaging of Subwavelength Resolution by Transformation Optics

Traditional magnifying glasses can give magnified virtual images with diffraction‐limited resolution, that is, detailed information is lost. Here, a novel magnifying glass by transformation optics, referred to as a “superresolution magnifying glass” (SMG) is designed, which can produce magnified virtual images with a predetermined magnification factor and resolve subwavelength details (i.e., light sources with subwavelength distances can be resolved). Based on theoretical calculations and reductions, a metallic plate structure to produce the reduced SMG in microwave frequencies, which gives good performance verified by both numerical simulations and experimental results, is proposed and realized. The function of SMG is to create a superresolution virtual image, unlike traditional superresolution imaging devices that create real images. The proposed SMG will create a new branch of superresolution imaging technology.     

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.     

辅以波谱分析的高分辨率影像面向对象分类研究

随着遥感影像空间分辨率的提高,地物的空间信息更加丰富,地物尺寸、形状以及相邻地物的关系得到更好的反映,因此目前高分辨率影像分类方法更侧重于利用地物的空间信息,分类过程中参与较多的人为主观因素,在地物类型未知的地区很难进行解译工作。另外,分割过于细碎导致操作数据量太大也是高分辨率影像分类的难题之一。论文提出了辅以波普分析的高分辨率影像面向对象分类方法,即在传统面向对象分类方法的基础上结合影像波谱分析,先对影像光谱角制图粗分类、掩膜操作,再面向对象精分类,较好解决了以往面向对象分类方法地物类型的不确定性和分割细碎等问题。试验以空间分辨率为0.5米的八波段WorldView2影像为研究数据提取西部那曲地区道路和河流,精度达到96.36%。     

Reconstructing the geometric configuration of three dimensional interface using electrical capacitance tomography

Electrical capacitance tomography is a promising visualization technique to image the internal permittivity distribution using boundary capacitance measurements. Because of its advantages of noninvasive, noninstructive, no radiation, and low cost, it has been successfully applied in many industrial processes. Currently, the commonly used algorithms in electrical capacitance tomography are based on the pixel/volume‐wise parameterization of the permittivity. When the permittivity is piecewise constant, it is difficult to enhance the spatial resolution. In the paper, a shape‐based algorithm is presented to directly reconstruct the geometric configuration of the smooth interface between two layered materials. By parameterizing the interface shape using Bézier surface, the unknown shape is iteratively approached using Levenberg–Marquardt method. To improve the computational efficiency, the forward problem is solved using a cornice boundary integral equation, and a fast Jacobian calculation method is derived using the reciprocity theorem and some integral transform technique. The numerical results demonstrate that the presented method has ability to reconstruct the smooth and continuous three‐dimensional interfaces with a good accuracy and high convergence, even when the permittivity values of the reconstructed substances are also unknown. Copyright © 2013 John Wiley & Sons, Ltd.     

A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity‐estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy‐based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 224–231, 2007     

Reduction of Noise‐Induced Bias in Displacement Estimation by Linear Off‐Pixel Digital Image Correlation

Abstract: A linear digital image correlation algorithm is proposed to eliminate noise‐induced bias in one‐dimensional translation estimation using noisy images. The algorithm uses linear interpolation for both initial and current images at off‐pixel positions and solves directly the displacement parameter by minimizing a sum‐of‐squared‐differences coefficient. Both analytical results and numerical simulations using synthetic image sets show that there is indeed no noise‐induced bias in the displacement estimation using the proposed algorithm if the off‐pixel positions in both images are chosen properly according to the relative displacement between two images. When the displacement is only known initially within a range of ±0.5 pixels from the actual displacement, an iterative procedure using the algorithm is able to obtain the displacement estimation with a residual bias that converges to the noiseless subpixel approximation bias. A further refinement of the off‐pixel analysis algorithm will be needed so the remaining residual bias due to subpixel approximation can also be significantly reduced.     

Cross‐Correlation and Differential Technique Combination to Determine Displacement Fields

Abstract: This study presents a method to measure the displacement fields on the surface of planar objects with sub‐pixel resolution, by combining image correlation with a differential technique. First, a coarse approximation of the pixel level displacement is obtained by cross‐correlation (CC). Two consecutive images, taken before and after the application of a given deformation, are recursively split in sub‐images, and the CC coefficient is used as the similarity measure. Secondly, a fine approximation is performed to assess the sub‐pixel displacements by means of an optical flow method based on a differential technique. To validate the effectiveness and robustness of the proposed method, several numerical tests were carried out on computer‐generated images. Moreover, real images from a static test were also processed for estimating the displacement resolution. The results were compared with those obtained by a commercial digital image correlation code. Both methods showed similar and reliable results according to the proposed tests.     

Aberration-free superresolution imaging via binary speckle pattern encoding and processing

We present an approach that provides superresolution beyond the classical limit as well as image restoration in the presence of aberrations; in particular, the ability to obtain superresolution while extending the depth of field (DOF) simultaneously is tested experimentally. It is based on an approach, recently proposed, shown to increase the resolution significantly for in-focus images by speckle encoding and decoding. In our approach, an object multiplied by a fine binary speckle pattern may be located anywhere along an extended DOF region. Since the exact magnification is not known in the presence of defocus aberration, the acquired low-resolution image is electronically processed via a parallel-branch decoding scheme, where in each branch the image is multiplied by the same high-resolution synchronized time-varying binary speckle but with different magnification. Finally, a hard-decision algorithm chooses the branch that provides the highest-resolution output image, thus achieving insensitivity to aberrations as well as DOF variations. Simulation as well as experimental results are presented, exhibiting significant resolution improvement factors.     

Medical image fusion based on improved sum‐modified‐Laplacian

Sum‐modified‐Laplacian (SML) plays an important role in medical image fusion. However, fused rules based on larger SML always lead to fusion image distortion in transform domain image fusion or image information loss in spatial domain image fusion. Combined with average filter and median filter, a new medical image fusion method based on improved SML (ISML) is proposed. First, a basic fused image is gained by ISML, which is used for evaluation of the selection map of medical images. Second, difference images can be obtained by subtracting average image of all sources of medical images. Finally, basic fused image can be refined by difference images. The algorithm can both preserve the information of the source images well and suppress pixel distortion. Experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art medical image fusion methods. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 206–212, 2015