A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

A novel image restoration approach based on point location in high-dimensional space geometry

The goal of image restoration is to restore the original clear image from the existing blurred image without distortion as possible. A novel approach based on point location in high-dimensional space geometry method is proposed, which is quite different from the thought ways of existing traditional image restoration approaches. It is based on the high-dimensional space geometry method, which derives from the fact of the Principle of Homology-Continuity (PHC). Begin with the original blurred image, we get two further blurred images. Through the regressive deducing curve fitted by these three images, the first iterative deblured image could be obtained. This iterative "blurring-debluring-blurring" process is performed till reach the deblured image. Experiments have proved the availability of the proposed approach and achieved not only common image restoration but also blind image restoration which represents the majority of real problems.

High-Dimensional Space Geometrical Informatics and its applications to image restoration

With a view to solve the problems in modern information science, we put forward a new subject named High-Dimensional Space Geometrical Informatics (HDSGI). It builds a bridge between information science and point distribution analysis in high-dimensional space. A good many experimental results certified the correctness and availability of the theory of HDSGI. The proposed method for image restoration is an instance of its application in signal processing. Using an iterative "further blurring-debluring-further blurring" algorithm, the deblured image could be obtained.

A novel image restoration algorithm based on high-dimensional space geometry

A novel image restoration approach based on high-dimensional space geometry is proposed, which is quite different from the existing traditional image restoration techniques. It is based on the homeomorphisms and "Principle of Homology Continuity" (PHC), an image is mapped to a point in high-dimensional space. Begin with the original blurred image, we get two further blurred images, then the restored image can be obtained through the regressive curve derived from the three points which are mapped form the images. Experiments have proved the availability of this "blurred-blurred-restored" algorithm, and the comparison with the classical Wiener Filter approach is presented in final.

A Novel Geometric Algorithm for Blind Image Restoration Based on High-Dimensional Space

A novel geometric algorithm for blind image restoration is proposed in this paper, based on High-Dimensional Space Geometrical Informatics (HDSGI) theory. In this algorithm every image is considered as a point, and the location relationship of the points in high-dimensional space, i.e. the intrinsic relationship of images is analyzed. Then geometric technique of "blurring-blurring-deblurring" is adopted to get the deblurring images. Comparing with other existing algorithms like Wiener filter, super resolution image restoration etc., the experimental results show that the proposed algorithm could not only obtain better details of images but also reduces the computational complexity with less computing time. The novel algorithm probably shows a new direction for blind image restoration with promising perspective of applications.

Optical image contrast reversal using bacteriorhodopsin films

The implementation of image contrast reversal by using a photochromic material of Bacteriorhodopsin (BR) films is demonstrated with two methods based on the optical properties of BR. One is based on the absorption difference between the B and M states. Images recorded by green light can be contrast reversed readout by violet light. The other is based on the photoinduced anisotropy of BR when it is excited by linear polarization light. By placing the BR film between two crossed polarizers (i.e. a polarizer and an analyser), the difference of polarization states of the recorded area and the unrecorded area can be detected, and thus different contrast images can be obtained by rotating the polarization axis of the analyser.

Synthesis and application on parallel image storage of bisfurylfulgide

A bisfurylfulgide, E, E-3,4-bis[1-(2,5-dimethyl-3-furyl)ethylidene]-3,4-dihydrofuran-2,5-dione, is synthesized by Stobbe condensation reaction. The molecular structure of target compound is confirmed by single crystal X-ray crystallography analysis. It shows that the distances between two possible reaction sites of molecule are 0.3394 and 0.3406 nm respectively, which is favorable to photocyclization. The photochromic properties of this compound in different solvents are investigated, and the result shows that the compound exhibits excellent photochromic behavior. The primary result of applied research on parallel image storage is also presented.

Optical image processing using the photoinduced anisotropy of pyrrylfulgide

A synthesized photochromic compound-pyrrylfulgide-is prepared as a thin film doped in a polymethylmethacrylate (PMMA) matrix. Under irradiation by UV light, the film converts from the bleached state into a colored state that has a maximum absorption at 635 nm and is thermally stable at room temperature. When the colored state is irradiated by a linearly polarized 650 nm laser, the film returns to the bleached state; photoinduced anisotropy is produced during this process. Application of optical image processing methods using the photoinduced anisotropy of the pyrrylfulgide/PMMA film is described. Examples in non-Fourier optical image processing, such as contrast reversal and image subtraction and summation, as well as in Fourier optical image processing, such as low-pass filtering and edge enhancement, are presented. (c) 2006 Optical Society of America.

A multi-frame image super-resolution method

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. (C) 2009 Elsevier B.V. All rights reserved.