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Title: IMAGEPROCESSING2
Description: IMAGEPROCESSING2

In this paper, the basics of capturing an image, image processing to modify and enhance the image are discussed. There are many applications for Image Processing like surveillance, navigation, and robotics. Robotics is a very interesting field and promises future development so it is chosen as an example to explain the various aspects involved in Image Processing . The various techniques of Image Processing are explained briefly and the advantages and disadvantages are listed. There are countless different routines that can be used for variety of purposes. Most of these routines are created for specific operations and applications. However, certain fundamental techniques such as convolution masks can be applied to many classes of routines. We have concentrated on these techniques, which enable us to adapt, develop, and use other routines and techniques for other applications. The advances in technology have created tremendous opportunities for visual system and image processing. There is no doubt that the trend will continue into the future.

The main of this paper is to presents a novel algorithm using color contrast enhancement and lacuna texture synthesis is proposed for the virtual restoration of ancient Chinese paintings. Color contrast enhancement based on saturation and de-saturation is performed in the u'v'Y color space. Additionally, this work presents a new patching method using the Markov Random Field (MRF) model of texture synthesis. The synthesization procedure integrates three key approaches, weighted mask, annular scan and auxiliary, with neighborhood searching. These approaches can maintain a complete shape and prevent edge disconnection in the final results.

I. INTRODUCTION

THE uses of digital processing for image improvement recently has received considerable interest and has broad applications in medical imagery, remote sensing, digital multimedia, image transmission, and so on. The digital processing includes four types of image manipulation processes, namely: image compression, image enhancement, image restoration and geometrical image modification. The main objective of image enhancement techniques is to process an image, and producing a new image that is more suitable than the original for certain specific applications. No general unifying theory and image quality standard exists that can suggest design criteria for image enhancement processing. Moreover, processing image enhancement for restoration and reconstruction becomes even more difficult when dealing with ancient paintings. Numerous techniques exist for image enhancement. Some of these techniques focus on emphasizing the local image sity or color variations to enhance perceptual visibility. Accordingly, most of these techniques work in some color spaces where the intensity and chromaticity components can be separated and adopted using the human visual percept sity or color variations to enhance perceptual visibility. Accordingly, most of these techniques work in some color spaces where the intensity and chromaticity components can be separated and adopted using the human visual perception model.

This study describes the hybrid method in the XYZ and L u'v' color spaces of the CIE primary color coordinate system, which enables the combination of the enhancement operations. Owing to the non uniform character of the commonly used xy chromaticity diagram, we propose selecting the more uniform u'v' chromaticity diagram. For many short lines in the xy chromaticity diagram joining a pair of points, which represent two colors with perceptual color difference of the same magnitude, these identical color differences should be represented by lines of equal length. In fact, these lines are much longer than the average length toward the green part of the spectral locus, and much shorter toward the violet part. Since, the ratio of the longest to the shortest line in the u'v' chromaticity diagram is only around four to one, instead of around twenty to one in the xy chromaticity diagram, color mixing performance based on color distance is better in the u'v' chromaticity diagram. Lucchese and Mitra originally proposed the nonlinear filtering and enhancement techniques for color images in the xy chromaticity diagram, adopts the saturation and de-saturation concept using the center of gravity law for color mixture , in the uniform u'v' chromaticity domain, and combines them with adaptive histogram modification to adjust the luminance component Y to a suitable brightness level. The final image is enhanced with more appealing color in the brighter region and more brightness contrast in the darker region. Sharpness also is increased and visible details of the resultant image.

Texture synthesis involves synthesizing numerous textures from original samples, and their structure characteristic is similar to sample structure. Efros and Wei use the Markov random field/Gibbs sampling, to model texture, and perform synthesis with probability sampling. Additionally, matching sample texture appearance using a pyramid-model, Heeger and Bergen developed an algorithm that obtained better-synthesized results ofrandom textures. Moreover, the approach of DeBonet preserved the cross-scale dependencies of the sample texture, and moreover works better on structured textures. The texture synthesis technique inspires us to repair ancient paintings, eliminating undesirable patterns and filling it with texture. The undesirable aged painting patterns displayed in generally include stains, crevices and artifacts (words or model. or signets), and so on. This study proposes a new texture synthesis algorithm using the weighted mask, annular scan and auxiliary to fix damage on paintings of various ages. The algorithm successfully restores the digital imageries of ancient Chinese paintings, and the experimental results demonstrate its superiority to previous techniques.

The remainder of this paper is organized as follows. First, Section II presents the hybrid color contrast enhancement method. Subsequently, Section III describes the proposed texture synthesis method. The experimental results then are presented in Sections IV. Finally, conclusions are given in Section V.

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