An Efficient Denoising Technique Using Filters With Noise Estimator
Prerna Sahu, Devanand Bhosle · Zenodo (CERN European Organization for Nuclear Research) · 2016
Magnetic resonance medical images are generally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. Development in computerized medical image reconstruction has make medical imaging into one of the most important sub-fields in scientific imaging. The quality of digital medical images become an important issue with the use of digital imaging to diagnose a disease. It is necessary that medical image must be clean, sharp and noise free to obtain a best possible diagnosis. As the technology became advance the quality of digital images continue to improve, the result is in improvement in the resolution and quality of images, removing noise from these images is one of the challenging task because they could blur and mask important parameter of the images. These are different images de-noising methods each having their own advantages and disadvantages. De-noising methods are often applied to increase the signal to noise ratio (SNR) and improve image quality. The search for efficient image de-noising methods is still a valid challenge at the crossing of functional analysis and statistic. Many de-noising methods have been developed over the years, among this method, wavelet thresholding is one of the most popular approaches. In wavelet thresholding a signal is decomposed into its approximation (Low frequency) and detail (high frequency) sub-bands; since most of the image information is connected in a few large coefficients, the detail sub-bands are processed with hard or soft thresholding operations.