A survey on methods for improving medical image resolution

Image Processing

Diagnostic quality is an essential requirement in the medical images compression field to avoid misdiagnosis by radiologists. In this paper, a novel study on using the logarithm in medical images compression is presented. In this approach the novel compression and resolution schemes are proposed to improve the image quality. The proposed compression schemes rely on 3D discrete wavelet transform, denoising, gamma correlation method and sparse dictionary representation method. With the increasing use of telemedicine there is a great demand in real-time processing and transmission of medical images. Noise is one of the important factors that degrade the quality of medical images. Impulse noise is a common noise that could be caused by malfunctioning of sensors or by data transmission errors. It is one the most common noises that have extensively been studied in recent years. For real-time noise removal hardware techniques are more suited, since software methods are complex and slow. Usually hardware techniques have low complexity and low accuracy. In this paper a low complexity, high accuracy, de-noising method is proposed.It first categorizes image pixels into a number of groups. Then noisy pixels are restored in different ways in each category. Local analysis of image blocks allows us to restore a noisy pixel by using its neighbouring non-noisy pixels. All steps are designed to have low hardware complexity. Simulation results show that in the case of MR images, the proposed method removes impulse noise with acceptable accuracy.

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