Narrow Results

Noise reduction in images, also known as image smoothing, is an essential and first step before further processings of the image. The key to image smoothing is to preserve important features while removing noise from the image. Gaussian function is widely used in image smoothing. Recently it has been reported that exponential functions (value of the exponent is not equal to 2) perform substantially better than Gaussian functions in modeling and preserving image features. In this paper we propose a family of exponential functions, that include Gaussian when the value of the exponent is 2, for image smoothing. We experiment with a variety of images, artificial and real, and demonstrate that optimal results are obtained when the value of the exponent is within a certain range.

This chapter considers image analysis and processing problems based on a mathematical model of the signal and two-dimensional variations. A two-scale image model is formulated, and the applications of twodimensional variations to discrete signals and image analysis are stated. The ways of evaluating the image complexity with the help of variations are explored. Changes in complexity estimates for different image transformations are analyzed. The issues of video signal separation into several information components are considered. An algorithm for image decomposition and extracting the component, which carries the basic information about the image objects, is given. An approach to formatting the topological characteristics of two-dimensional signals is proposed, and their use for estimating image parameters is considered. The index of object size and amplitude of convexity is introduced; their application for detecting the noise and objects of different sizes is demonstrated. During the formation, an image could be subjected to various distortions. The way to remove distortions is to solve the inverse problem. The most important task is the determination of the type and parameters of the distortion process. The task of diagnosing the distortion operator by analyzing the obtained image is discussed.

Smoothing on the sedimentary model with SIS method based on MAPS can make the realization more close to geological acknowledge, what is more, reproducing space distribution and characterization of sedimentary facies model more accurately. In this paper, taking fluvial reservoir as an example, selecting the sand body volumes connected to wells and number of independent sand bodies as experiment target. 6 trials are designed to determine main factors and levels. At first, Fluvism algorithm is a better method to achieve a simulation of river sand body that have been choosed. Then condition information which is used for SIS simulation can be obtained with appropriate well space. At last, Petrel model software is used to simulate and calculate various testing programs. According to the variety of the experiment target during the experiment process, effect on sedimentary facies model controlled by smoothing process can be analysed and evaluated.