Hardware based Brain MR Image De-Noising using Weiner filter with Discrete Haar Wavelet Transform
Srinivasan Aruchamy, Partha Bhattacharjee, Goutam Sanyal · International Journal of Bio-Science and Bio-Technology · 2017
Noise is a serious issue in any brain MR image analysis.Additive noises like Gaussian noise, salt and pepper noise and multiplicative noise like speckle noise are most common noises make MR image to suffer in diagnosis.In brain image analysis, MR image denosing plays an important role.Image de-nosing step improves the image quality by removing unwanted noise present in the image by applying some transformation techniques without losing the useful information.In the proposed work an attempt has been made to study different noise models like additive random noise, impulse noise, multiplicative noise and haar discrete wavelet transform combination with weiner filter has been presented.An attempt has been made to implement the same in hardware platform and study the performance of the implemented algorithm.Results were compared with several performance metrics like PSNR, Mean square error (MSE), Absolute Mean square error (AMBE), Structural similarity Index (SSIM).It has been implemented in a single board computer (raspberry pi) open source software platform OpenCV.