Image denoising review: From classical to state-of-the-art approaches

Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used). The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (StationaryWavelet Transform). We used the SSIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) and SSIM map to assess the quality of denoised images.

Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter

Speckle noise affects all coherent imaging systems including medical ultrasound. In medical images, noise suppression is a particularly delicate and difficult task. A tradeoff between noise reduction and the preservation of actual image features has to be made in a way that enhances the diagnostically relevant image content. Even though wavelets have been extensively used for denoising speckle images, we have found that denoising using contourlets gives much better performance in terms of SNR, PSNR, MSE, variance and correlation coefficient. The objective of the paper is to determine the number of levels of Laplacian pyramidal decomposition, the number of directional decompositions to perform on each pyramidal level and thresholding schemes which yields optimal despeckling of medical ultrasound images, in particular. The proposed method consists of the log transformed original ultrasound image being subjected to contourlet transform, to obtain contourlet coefficients. The transformed image is denoised by applying thresholding techniques on individual band pass sub bands using a Bayes shrinkage rule. We quantify the achieved performance improvement. Keywords—Contourlet transform, Despeckling, Pyramidal directional filter bank, Thresholding.

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Speckle Reducing Contourlet Transform for Medical Ultrasound Images

In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image, a denoising method of medical image based on discrete wavelet transform (DWT) and modified median filter for medical image coupling denoising is proposed. The method is composed of four modules: image acquisition, image storage, image processing and image reconstruction. Image acquisition gets the medical image that contains Gaussian noise and impulse noise. Image storage includes the preservation of data and parameters of the original image and processed image. In the third module, the medical image is decomposed as four sub bands (LL, HL, LH, HH) by wavelet decomposition, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and in the diagonal line high frequency component. Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients, the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing. The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction. The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better, not a simple combination of the two previous methods. With DWT and improved median filter coefficients coupling denoising, it is highly practical for high-precision medical images containing complex noises. The experimental results of proposed algorithm are compared with the results of median filter, wavelet transform, contourlet and DT-CWT, etc. According to visual evaluation index PSNR and SNR and Canny edge detection, in low noise images, PSNR and SNR increase by 10%–15%; in high noise images, PSNR and SNR increase by 2%–6%. The experimental results of the proposed algorithm achieved better acceptable results compared with other methods, which provides an important method for the diagnosis of medical condition.

Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image

Image denoising is the process to remove the noise from the image naturally corrupted by the noise The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images,  etc The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage (where either hard and soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used) The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (Stationary Wavelet Transform) We used the SSIM (Structural Similarity Index Measure) along with PSNR (Peak Signal to Noise Ratio) and SSIM Map to assess the quality of denoised images

Medical image denoising using dual tree complex thresholding wavelet transform

Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform. Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio (PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.

/pdf/medical-image-denoising-using-adaptive-threshold-based-on-7r2tfdl581.pdf

Medical Image Denoising using Adaptive Threshold Based on Contourlet Transform

/pdf/medical-image-denoising-using-adaptive-threshold-based-on-265z70qgcq.pdf

K. V. S. V. R. Prasad

Papers

Medical Image Denoising using Adaptive Threshold Based on Contourlet Transform

Medical image denoising using adaptive threshold based on contourlet transform