Implementation of Hybrid Error Model Using Super Resolution for Medical Images
Navdeep Kaur, Usvir Kaur, Sri Guru · 2013
In pattern recognition, for various domains different models or combination of models can be used. In case of noisy patterns, choice of statistical model is a good solution. Practical importance of structural model depends upon recognition of simple pattern primitives and their relationships represented by description language. Hybrid model is combination of both statistical and structure model. So it is best method to solve the many problems. In hybrid model, we use hybrid error model for super resolution of images Conventional X-ray imaging is the fastest, most common, and least expensive diagnostic imaging system available. The aim of this paper is to present a model using super resolution for removing the noise in digital X-ray images .The resulting X-ray images are more visible ,noise is reduced from X- ray images. We implement the model using frequency domain instead maximum likelihood because it gives better results in medical images. With the help of super resolution, we increase the resolution of the image that also increases the detail of the image. When we remove the noise from the image, image quality also increases that helps us to find the clearly symptom of any diseases.