We would also like to thank our project co-coordinator Assistant Professor Mr… G. Ravishing for his kind encouragement and overall guidance in viewing this project a good asset. Finally we would like to thank our family members who stood by us and encouraged us to work hard in order to achieve success. ABSTRACT: Digital images play an important role both in daily life applications such as satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and technology such as geographical information systems and astronomy.
Data sets collected by image sensors are generally contaminated by noise. Imperfect instruments, problems with the data acquisition process, and interfering natural phenomena can all degrade the data of interest. Furthermore, noise can be introduced by transmission errors and compression. Thus, dimension is often a necessary and the first step to be taken before the images data is analyzed. It is necessary to apply an efficient dimension technique to compensate for such data corruption. Image dimension still remains a challenge for researchers because noise removal introduces artifacts and causes blurring of the images.
This project describes different methodologies for noise reduction (or dimension) giving an insight as to which algorithm should be used to find the most reliable estimate of the original image data given its degraded version. Noise modeling in images is greatly affected by capturing instruments, data transmission media, image quantization and discrete sources of radiation. Different algorithms are used depending on the noise model. Most of the natural images are assumed to have additive random noise which is modeled as a Gaussian. Speckle noise is observed in images. The scope of the project is to focus on noise removal techniques for images.