A new technique for embedding image data into another BMP image data is presented. The image data to be embedded is referred to as signature image, while the image into which the signature image is embedded is referred as host image. The host and the signature images are first partitioned into 8x8 blocks, discrete cosine transformed “DCT”, only significant coefficients are retained, the retained coefficients then inserted in the transformed block in a forward and backward zigzag scan direction. The result then inversely transformed and presented as a BMP image file. The peak signal-to-noise ratio (PSNR) is exploited to evaluate the objective visual quality of the host image compared with the original image.

     Today in the digital realm, where images constitute the massive resource of the social media base but unfortunately suffer from two issues of size and transmission, compression is the ideal solution. Pixel base techniques are one of the modern spatially optimized modeling techniques of deterministic and probabilistic bases that imply mean, index, and residual. This paper introduces adaptive pixel-based coding techniques for the probabilistic part of a lossy scheme by incorporating the MMSA of the C321 base along with the utilization of the deterministic part losslessly. The tested results achieved higher size reduction performance compared to the traditional pixel-based techniques and the standard JPEG by about 40% and 50%,

Spatial and frequency domain techniques have been adopted in this search. mean
value filter, median filter, gaussian filter. And adaptive technique consists of
duplicated two filters (median and gaussian) to enhance the noisy image. Different
block size of the filter as well as the sholding value have been tried to perform the
enhancement process.

The Adaptive Optics technique has been developed to obtain the correction of atmospheric seeing. The purpose of this study is to use the MATLAB program to investigate the performance of an AO system with the most recent AO simulation tools, Objected-Oriented Matlab Adaptive Optics (OOMAO). This was achieved by studying the variables that impact image quality correction, such as observation wavelength bands, atmospheric parameters, telescope parameters, deformable mirror parameters, wavefront sensor parameters, and noise parameters. The results presented a detailed analysis of the factors that influence the image correction process as well as the impact of the AO components on that process

Based on collections made during March to September 2012. A totals of 58 species belong to 33 genera were identified from extra north to extra south of Erbil governorate, among them 30 species are registered as a new record to flora of Iraq. Most attention was paid to the most common and abundant lichens that present almost in most locations, which were Collema cristatum, Diploschistes scruposus, Lecanora dispersa, Lecanora murales, Pertusaria flavicunda, Placidium lacinulatum, Thelomma californicum and Verrucaria Maura.

Landsat-5 Thematic Mapper (TM) has been imaging the Earth since March 1984 and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) was added to the series of Landsat instruments in April 1999. In this paper the two sensors are used to monitoring the agriculture condition and detection the changing in the area of plant covers, the stability and calibration of the ETM+ has been monitored extensively since launch although it is not monitored for many years, TM now has a similar system in place to monitor stability and calibration. By referring to statistical values for the classification process, the results indicated that the state of vegetation in 1990 was in the proportion of 42.8%, while this percentage rose to 52.5% for the same study area in

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).