Abstract

Image processing is the emerging field where it is possible to increase or decrease the quality of image without affecting the required features or objects in the image for particular applications. It includes the fields of medical, astronomy, satellites, etc., particularly in the digital medical imaging technologies such as Computerized Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), as well as combined modalities, e.g. SPECT/CT has revolutionized modern medicine. Resolutions of MRI medical images are very much intended for diagnosis and curing of physical and metabolic activities of the volume below the skin. Many image processing techniques for increasing the MRI image resolution are proposed which are not suitable for noise corrupted images. To provide solution for this, Super- Resolution techniques are introduced, all the Super-Resolution techniques are based on multiple images. After that, sparse representation techniques are proposed which are the single image super resolution methods, but it is based on the constructed databases, which require large computational tasks, and also it is a time consuming process. The proposed method contains only a single image which can be super resolved and denoised by the different techniques with increased PSNR (Peak Signal to Noise Ratio) and also compromising SSIM (Structural Similarity) with better visibility objects in the MRI image.

Keywords
Cubic Spline Interpolation, Image Denoising, Switched Bilateral Filter (SBF), Super Resolution, Field Programmable Gate Array (FPGA)

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