Image quality assessment metrics by using directional projection

Objective image quality mcasure, which is a fundamental and challenging job in image processing, evaluates the image quality consistently with human perception automatically. On the assumption that any image distortion could be modeled as the difference between the directional projection-based maps of reference and distortion images, wc propose a new objective quality assessment method based on directional projection for full reference model. Experimental results show that the proposed metrics are well consistent with the subjective quality score.     

On images of Borel measures under Borel mappings

Let and be metric spaces. We show that the tight images of a (fixed) tight Borel probability measure on , under all Borel mappings , form a closed set in the space of tight Borel probability measures on with the weak-topology. In contrast, the set of images of under all continuous mappings from to may not be closed. We also characterize completely the set of tight images of under Borel mappings. For example, if is non-atomic, then all tight Borel probability measures on can be obtained as images of , and as a matter of fact, one can always choose the corresponding Borel mapping to be of Baire class 2.

     

超半球型浸没透镜在指纹图像采集中的应用

简要地叙述了指纹图像的应用意义。介绍了一台指纹采集装置 ,它主要由全反射棱镜、反射镜、双胶合透镜、超半球型浸没透镜、CCD等组成 ,并与计算机接口 ,从而实现了指纹采集过程的数字化 ,可以实时显示、存储指纹。着重地描述了超半球型浸没透镜的原理和成像特点。展示了几幅由指纹采集系统采集的指纹图像     

Bestimmung von Diffusionskoeffizientcn an Autoradiogrammen mit Hilfe von Schwärzungsmomenten

Zur experimentellen Bestimmung des Diffusionskoeffizienten in Festkörpermaterialien werden sehr häufig autoradiographische Verfahren in der Radionuklid-Tracertechnik angewendet. Bedingt durch die autoradiographische Abbildung ist es im allgemeinen nicht möglich, den Diffusionskoeffizienten direkt aus dem Schwārzungsverlauf des Autoradiogramms abzulesen.

Aus diesem Grunde wurde ein mathematisches Verfahren entwickelt, das den Diffusionskoeffizienten in Abhängigkeit von Schwärzungsmomenten liefert, die nach einer bestimmten Vorschrift zu bilden sind. Die Lösung der autoradiographischen Abbildungsgleichung konnte dabei umgangen werden.

Das Verfahren wird an einem typischen Beispiel erläutert.     

Fusion method for infrared and visible images by using non-negative sparse representation

In this paper, an interesting fusion method, named as NNSP, is developed for infrared and visible image fusion, where non-negative sparse representation is used to extract the features of source images. The characteristics of non-negative sparse representation coefficients are described according to their activity levels and sparseness levels. Multiple methods are developed to detect the salient features of the source images, which include the target and contour features in the infrared images and the texture features in the visible images. The regional consistency rule is proposed to obtain the fusion guide vector for determining the fused image automatically, where the features of the source images are seamlessly integrated into the fused image. Compared with the classical and state-of-the-art methods, our experimental results have indicated that our NNSP method has better fusion performance in both noiseless and noisy situations.     

A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA

The traditional noise reduction methods for 3-D infrared hyperspectral images typically operate independently in either the spatial or spectral domain, and such methods overlook the relationship between the two domains. To address this issue, we propose a hybrid spatial-spectral method in this paper to link both domains. First, principal component analysis and bivariate wavelet shrinkage are performed in the 2-D spatial domain. Second, 2-D principal component analysis transformation is conducted in the 1-D spectral domain to separate the basic components from detail ones. The energy distribution of noise is unaffected by orthogonal transformation; therefore, the signal-to-noise ratio of each component is used as a criterion to determine whether a component should be protected from over-denoising or denoised with certain 1-D denoising methods. This study implements the 1-D wavelet shrinking threshold method based on Stein’s unbiased risk estimator, and the quantitative results on publicly available datasets demonstrate that our method can improve denoising performance more effectively than other state-of-the-art methods can.     

Aircraft recognition based on improved iterative threshold selection and skeleton Zernike moment

Using the visible optics images to identify targets is an important part in the development of remote sensing technology. In this paper, a new aircraft recognition method based on the improved iterative threshold selection and the skeleton Zernike moment is presented. The method segment aircraft targets under complex background using iterative threshold selection with between-class distance and scatter, and calculate the skeleton Zernike moment for the aircraft target recognition using template matching method. The experimental results show that the new method can effectively achieve the target segmentation under complex backgrounds, and provide a satisfactory performance both in recognition rate and recognition speed.