Yes / In this study a method for 3D representation of active regions and sunspots that are detected from Solar and Heliospheric Observatory/Michelson Doppler Imager magnetogram and continuum images is provided. This is our first attempt to create a visual solar catalogue. Because of the difficulty of providing a full description of data in text based catalogues, it can be more accurate and effective for scientist to search 3D solar feature models and descriptions at the same time in such a visual solar catalogue. This catalogue would improve interpretation of solar images, since it would allow us to extract data embedded in various solar images and visualize it at the same time. In this work, active regions that are detected from magnetogram images and sunspots that are detected from continuum images are represented in 3D coordinates. Also their properties extracted from text based catalogues are represented at the same time in 3D environment. This is the first step for creating a 3D solar feature catalogue where automatically detected solar features will be presented visually together with their properties.

Life on Earth is strongly related to the Sun, which makes it a vital star to study and understand. To improve our knowledge of the way the Sun works, many satellites have been launched into space to monitor the Sun‟s activities where the one of main focus is the effect of these activities on the Earth‟s climate; PICARD is one such satellite. Due to the noise associated with SODISM images, the clarity of these images and the appearance of solar features are affected. Image denoising and enhancement are the main techniques to improve the visual appearance of SODISM images. Affective de-noising algorithm methods depend on a proper detecting of noise present in the image. The aim is to identify which type of noise is present in the image. To reach this point, supervised machine-learning (ML) classifier is used to classify the type of noise present in the image. Furthermore, this work introduces a novel technique developed to enhance the quality of SODISM images. In this thesis, the Modified Undecimated Discrete Wavelet Transform (M-UDWT) technique is used to de-noise and enhance the quality of SODISM images. The proposed method is robust and effectively improves the quality of SODISM images, and produces more precise information and clear feature are brought out. In addition, the non wavelet enhancement is developed as well in this thesis. The results of this algorithm is discussed. The new methods are also assessed using two different methods: subjective (by human observation) and objective (by calculation)