I. INTRODUCTION:

Nowadays more and more discrepancy exists between recorded color images and the direct observation. Human perception excels at constructing a visual representation with vivid color and detail across the wide ranging photometric levels due to lighting variations. In addition[7], human vision computes color so as to be relatively independent of spectral variations in illumination[2]. That is color constant. The recorded images of film and electronic cameras suffer, by comparison, from a loss in clarity of detail and color as light levels drop within shadows, or as distance from a lighting source increases. Likewise, the appearance of color in recorded images is strongly influenced by spectral shifts in the scene illuminant. We refer to the computational analog to human vision color constancy as color consistency. When the dynamic range of a scene exceeds the dynamic range of the recording medium, there is an irrevocable loss of visual information[3] at the extremes of the scene dynamic range. Therefore, improved fidelity of color images to human observation demands:

II. FOLLOWING IS THE LIST OF DIFFERENT IMAGE:

o Image Acquisition

o Image Enhancement

o Image Restoration

o Color Image Processing

o Multi-resolution Processing

o Compression

o Morphological Processing

o Segmentation

o Representation and Description

o Object Recognition

III. IMAGE RESTORATION:

The field of digital image restoration had its first encounter with the starting of space program by the scientists involved of United States of America and the former Soviet Union in the 1950s and early 1960s. The first images of the Earth, Moon (mainly of the opposite side), and planet Mars were, at that time, of unimaginable resolution which were obtained under big technical difficulties. These programs were responsible for producing many incredible images of our solar system, which were at that time unimaginable. However, the images obtained from the various planetary missions of the time were subject to many photographic degradations [6]. The need to retrieve as much information as possible from such degraded images was the aim of the early efforts to adapt the one-dimensional signal processing algorithms to images, creating a new field that is today known as digital image restoration. The 22 pictures produced during the Mariner IV flight to Mars in1964 were later estimated to cost almost $10 million just in terms of the number of bits transmitted alone.

IV. METHOD:

Retinex Image Enhancements

1. A method bridging the gap between images and the human observation of scenes.

2. The retinex is an image enhancement algorithm that improves the brightness, contrast and sharpness of an image.

3. It performs a nonlinear spatial/spectral transform that provide simultaneous dynamic range compression and color constancy.

4. The algorithm is based on model of human visions lightness and color constancy.

5. The retinex is aimed to obtain the balance between the human vision and machine vision system along with color constancy.

Classification of retinex techniques

1. Single Scale Retinex(SSR).

2. Multi Scale Retinex(MSR).

3. Multi Scale Retneix with color restoration(MSR-CR).

4. Multi Scale Retinex with canonical gain/offset

Single scale retinex

· Single scale retinex seemed incapable of simultaneously providing sufficient dynamic range.

· Violation of grey world assumption led to retinexed image which were either grayed out locally or globally or more rarely, suffered from color distortion

Original Image

Multi Scale Retinex with Color Restoration

MSR as a method of image enhancement which provides color constancy and dynamic range compression. There are a number of problems with the original MSR method. The chief conceptual problem is that a number of image-processing tasks are performed simultaneously without sufficient regard to the interactions occurring between them. The main practical consequence of this is that MSR is not appropriate for applications which are sensitive to color.

V. CONCLUSION:

The work in the present thesis primarily focuses on retinex theory of image enhancement. The work reported in this thesis is summarized in this chapter. The chapter also presents pros and cons of the work. The last section provides some scope for further development.

VI. ACHIEVEMENTS AND LIMITATIONS OF THE WORK:

The present work mostly deals with color image enhancement. Two schemes have been employed to achieve enhancement with acceptable results.

VII. FUTURE WORK:

As it has been stated that the existing as well as proposed techniques are computationally expensive, investigation may be carried out in this direction. Development of parallel algorithms can also be done to counter attack the computational overhead.

VIII. REFERENCES:

[1] “Recent advances in retinex theory and some implications for cortical computations,” Proc. Nat. Acad. Sci.,vol. 80, pp. 5163.

[2] D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,”Image Processing,vol. 6, pp. 451–462, Mar. 1997.

[3] Z. Rahman, D. Jobson, and G. Woodell, “Retinex processing for automatic image enhancement, "in Journal of Electronic Imaging, 13, No. 1,pp. 100–110, January 2004.

[4] E. Land, “An alternative technique for the computation of the designator in the retinex theory of color vision,” in Proceedings of the National Academy of Science, 83, pp. 3078–3080, 1986.

[5] B. Funt, V. Cardei, and K. Barnard, ÒLearning Color Constancy,Ó Proc. Fourth IS&T/SID Color Imaging Conf., pp. 58-60, Scottsdale, Nov. 19-22,

1996.

[6] B. V. Funt, K. Barnard, M. Brockington, and V. Cardei, "Luminance-based multi-scale Retinex," Proceedings AIC Color 97, Kyoto, Japan, May 25-

30 (1997).

[7] The Wikipedia the Free Encylopedia Website. http://en.wikipedia.org.

[8] S. Iyer and S. V. Gogawale. Image Enhancement and Restoration Techniques in Digital Image Processing. Computer Society of India Communications, pages 6 – 14, June 1996.

Received on 17.10.2010 Accepted on 26.10.2010

Research J. Engineering and Tech. 1(2): Oct. - Dec.2010 page 55-57