Studies on the restoration succession of PFU microbial communities in a pilot-scale microcosm

In order to imitate the restoration succession process of natural water ecosystem, a laboratory microcosm system of constant-flow-restoration was designed and established. A eutrophycation lake, Lake Donghu, was selected as the subject investigated. Six sampling stations were set on the lake, among which the water of station IV was natural clean water, and others were polluted with different degrees. Polyurethane foam unit microbial communities, which had colonized in the stations for a month, were collected from these stations and placed in their respective microcosms, using clean water of station IV to gradually replace the water of these microcosms. In this process, the healthy community in clean water continuously replaced the damaged communities in polluted water, the restoration succession of the damaged communities was characterized by weekly determination of several functional and structural community parameters, including species number (S), diversity index (DI), community pollution value (CPV), heterotrophy index (HI), and similarity coefficient. Cluster analysis based on similarity coefficient was used to compare the succession discrepancies of these microbial communities from different stations. The ecological succession of microbial communities during restoration was investigated by the variable patterns of these parameters, and based on which, the restoration standards of these polluted stations were suggested in an ecological sense. That was, while being restored, the water of station 0 (supereutrophycation) should be substituted with natural clean water by 95%; station I (eutrophycation), more than 90%; station II (eutrophycation), more than 85%; station III (eutrophycation), about 85%; station V (mesoetitrophycation), less than 50%. The effects of the structural and functional parameters in monitoring and assessing ecological restoration are analyzed and compared. (C) 2007 Elsevier Ltd. All rights reserved.

The restoration of aquatic macrophytes for improving water quality in a hypertrophic shallow lake in Hubei Province, China

This paper deals with a case study of the restoration of submerged macrophytes for improving water quality in a hypertrophic shallow lake, Lake Donghu of Wuhan, Hubei Province, China. Macrophyte restoration experiments were conducted in large-scale enclosures established in three sublakes of different trophic status, and the effectiveness for water quality improvement was tested by using the enclosure experiment in the hypertrophic sublake. Water quality was remarkably improved after the reestablishment of aquatic macrophytes. It is suggested that the submerged vegetation of less polluted sublakes could be capable of recovering spontaneously once the stocking of herbivorous fishes has been ceased, and the K-selected plants such as Potamogeton maackianus should be introduced into these sublakes to enhance the stability of aquatic vegetation. However, it may not be possible and economical to restore the submerged macrophytes in severely polluted basins unless external pollution has been cut off and internal nutrient loadings considerably reduced. In this case, the r-selected submerged plants should be used as the pioneer species for macrophyte recovery. (C) 2001 Elsevier Science B.V. All rights reserved.

Blind image restoration based on the theory of high-dimensional space geometry

In practical situations, the causes of image blurring are often undiscovered or difficult to get known. However, traditional methods usually assume the knowledge of the blur has been known prior to the restoring process, which are not practicable for blind image restoration. A new method proposed in this paper aims exactly at blind image restoration. The restoration process is transformed into a problem of point distribution analysis in high-dimensional space. Experiments have proved that the restoration could be achieved using this method without re-knowledge of the image blur. In addition, the algorithm guarantees to be convergent and has simple computation.

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 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.