Comparison of Super Resolution Methods in Magnetic Resonance Images for Small Animals Super resolution (SR) is an array of methods that utilize different approaches to enhance the quality and resolution of an image. This is particularly useful for images that are very small and have low quality. Small images are usually obtained due to the limitation of imaging capture systems or the subject captured is small. For small animals such as rats, imaging can be difficult and expensive to produce highresolution images. Therefore, SR is a very relevant field of study for small animals. The study of small animals involve many impactful fields such as testing of harmful chemicals on the biological processes. The objective of this study is to compare 11 SR methods for rat magnetic resonance images (MRI). This study is a pilot study and the beginning of the research to see the effect of kratom that is a hallucinogen misused in south east Asia. This study used chosen images from six rats. These MRI images were captured at UM MRI Research Centre. This study compared the quality of SR methods using several measures including Peak Sound to Noise Ratio (PSNR), Sound to Noise Ratio (SNR), Mean Square Error (MSE), Structural Similarity Index (SSI). This study compared these methods on two different size factors of resolution which were two and four. The results show promising results for the next stage of research.

An Efficient Approach for Image Resolution Enhancement Using Multi Step Magnification Method The lowresolution image is viewed as down sampled version of a highresolution image, whose patches are assumed to have a sparse representation with respect to an overcomplete dictionary of prototype signalatoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the down sampled signal. We will demonstrate the effectiveness of sparsity as a prior for regularizing the otherwise illposed superresolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high resolution image is competitive or even superior in quality to images produced by other SR methods.