الفهرس 
CHAPTER 1: INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
When a laser beam is allowed to be aimed to an object that is optically rough (which its surface topography is in order of the wavelength of the incident light beam). This light is reflected or transmitted randomly with changes in the path differences. In this time interference occurs forming bright and dark spots. Bright spots are formed for constructive interference while Dark spots are form for destructive interference. These were defined as “Laser Speckles”. Surface roughness parameters can be used for characterization of the statistical roughness parameters represented in the arithmetic mean roughness Ra and the root mean square roughness Rq. In engineering Ra is better to use for characterization, But the optics science most frequently prefers to use Rq.
The first chapter represents an introduction to thesis with historical review for speckle techniques and applications.
The second chapter represents the geometry of the surface roughness with the roughness parameters. In optics,
Scientists concerned with the root mean square roughness Rq. The root mean square roughness is measured using stylus profile meter, but this tool is very expensive and needs to specific room conditions. The formation of speckle image depends on the value of Rq. this chapter introduces speckle formation with two ways “Objective” or “Subjective”. speckle image has its own parameters speckle size, contrast, and optical density.
The third chapter represents the recording of the laser speckle images formed by different rough surfaces, which are introduced to processing in the PC programs and characterizing the effectiveness of changing the roughness
to these parameters. The processing of speckle image is concerned with MATLAB and IMAGE PRO-PLUS, to get the experimental results for a high roughness surface of aluminum samples that are 25 μm, 50 μm, 75 μm, and 100 μm. On the other hand, the thesis concerned with the small rough surfaces of the glass with roughness 0.1 μm, 0.34 μm, 0.4 μm, 0.5 μm, and 1 μm.
The processing of the speckle image on PC occurs by using MATLAB program or IMAGE PRO-PLUS. The thesis concerned to make the compression between these programs.
The MATLAB can determine the original contrast, make a data compression for the speckle image, block processing of effectiveness. The IMAGE PRO-PLUS can determine the original contrast, and the optical density without control of the threshold, which means that the domain of MATLAB is better than this program.
The obtained phenomena to the small roughness samples, is the diffraction diffuse patterns around the speckle spots. This phenomenon is studied in the chapter.
The fourth chapter represents the speckle image in binary representation. All intensities in the speckle matrix are 0’s and 1’s only. This transformation occurs due to a specific condition called “Threshold”. The threshold is defined as a value of intensity among 0 to 255, MATLAB test all intensities in the speckle’s matrix. For intensity less than or equal threshold value, it replaced by 0, else, it replaced by 1. The binary speckle image contributes to optical density evaluations. The optical density values are roughness dependent at a certain threshold. The value of threshold may be independent (constant), or dependent i.e., maximum intensity dependent.
The fifth chapter concerned to the conclusion of the thesis and the observed from the experimental calculations with outlook for the future work.
Finally, the APPENDIX represents the MATLAB codes divided into three parts A, B, and C, the APPENDIX A is built to show the speckle image after data compressions at different dimensions also to evaluate the contrast of the original speckle image and the contrast after applying data compressions 10 × 100, 10 × 80, 10 × 60, and 10 × 40. For the APPENDEX B, it is designed to apply the block processing effectiveness for different blocks sizes such as 2
× 2, 4 × 4, 5 × 5, and 10 × 10 then calculates the contrast for each image. In APPENDIX C, the MATLAB shows the speckle image in the binary representation for different thresholds then, calculates the optical density for each image.