Showing 5 results for Denoising
E. Ehsaeyan,
Volume 12, Issue 1 (3-2016)
Abstract
The use of wavelets in denoising, seems to be an advantage in representing well the details. However, the edges are not so well preserved. Total variation technique has advantages over simple denoising techniques such as linear smoothing or median filtering, which reduce noise, but at the same time smooth away edges to a greater or lesser degree. In this paper, an efficient denoising method based on Total Variation model (TV), and Dual-Tree Complex Wavelet Transform (DTCWT) is proposed to incorporate both properties. In our method, TV is employed to refine low-passed coefficients and DTCWT is used to shrink high-passed noisy coefficients to achieve more accurate image recovery. The efficiency of our approach is firstly analyzed by comparing the results with well-known methods such as probShrink, BLS-GSM, SUREbivariate, NL-Means and TV model. Secondly, it is compared to some denoising methods, which have been reported recently. Experimental results show that the proposed method outperforms the Steerable pyramid denoising by 8.5% in terms of PSNR and 17.5% in terms of SSIM for standard images. Obtained results convince us that the proposed scheme provides a better performance in noise blocking among reported state-of-the-art methods.
E. Ehsaeyan,
Volume 12, Issue 2 (6-2016)
Abstract
Traditional noise removal methods like Non-Local Means create spurious boundaries inside regular zones. Visushrink removes too many coefficients and yields recovered images that are overly smoothed. In Bayesshrink method, sharp features are preserved. However, PSNR (Peak Signal-to-Noise Ratio) is considerably low. BLS-GSM generates some discontinuous information during the course of denoising and destroys the flatness of homogenous area. Wavelets are not very effective in dealing with multidimensional signals containing distributed discontinuities such as edges. This paper develops an effective shearlet-based denoising method with a strong ability to localize distributed discontinuities to overcome this limitation. The approach introduced here presents two major contributions: (a) Shearlet Transform is designed to get more directional subbands which helps to capture the anisotropic information of the image; (b) coefficients are divided into low frequency and high frequency subband. Then, the low frequency band is refined by Wiener filter and the high-pass bands are denoised via NeighShrink model. Our framework outperforms the wavelet transform denoising by %7.34 in terms of PSNR (peak signal-to-noise ratio) and %13.42 in terms of SSIM (Structural Similarity Index) for ‘Lena’ image. Our results in standard images show the good performance of this algorithm, and prove that the algorithm proposed is robust to noise.
E. Ehsaeyan,
Volume 13, Issue 3 (9-2017)
Abstract
Image denoising as a pre-processing stage is a used to preserve details, edges and global contrast without blurring the corrupted image. Among state-of-the-art algorithms, block shrinkage denoising is an effective and compatible method to suppress additive white Gaussian noise (AWGN). Traditional NeighShrink algorithm can remove the Gaussian noise significantly, but loses the edge information instead. To overcome this drawback, this paper aims to develop an improvement shrinkage algorithm in the wavelet space based on the NeighSURE Shrink. We establish a novel function to shrink neighbor coefficients and minimize Stein’s Unbiased Risk Estimate (SURE). Some regularization parameters are employed to form a flexible threshold and can be adjusted via genetic algorithm (GA) as an optimization method with SURE fitness function. The proposed function is verified to be competitive or better than the other Shrinkage algorithms such as OracleShrink, BayesShrink, BiShrink, ProbShrink and SURE Bivariate Shrink in visual quality measurements. Overall, the corrected NeighShrink algorithm improves PSNR values of denoised images by 2 dB.
O. Mahmoudi Mehr, M. R. Mohammadi, M. Soryani,
Volume 19, Issue 3 (9-2023)
Abstract
Speckle noise is an inherent artifact appearing in medical images that significantly lowers the quality and accuracy of diagnosis and treatment. Therefore, speckle reduction is considered as an essential step before processing and analyzing the ultrasound images. In this paper, we propose an ultrasound speckle reduction method based on speckle noise model estimation using a deep learning architecture called “speckle noise-based inception convolutional denoising neural network" (SNICDNN). Regarding the complicated nature of speckle noise, an inception module is added to the first layer to boost the power of feature extraction. Reconstruction of the despeckled image is performed by introducing a mathematical method based on solving a quadratic equation and applying an image-based inception convolutional denoising autoencoder (IICDAE). The results of various quantitative and qualitative evaluations on real ultrasound images demonstrate that SNICDNN outperforms the state-of-the-art methods for ultrasound despeckling. SNICDNN achieves 0.4579 dB and 0.0100 additional gains on average for PSNR and SSIM, respectively, compared to other methods. Denoising ultrasound based on its noise model estimation is not only a novel approach in comparison to traditional denoising autoencoder models but also due to the fact that it uses mathematical solutions to recover denoised images, SNICDNN shows a greater power in ultrasound despeckling.
Mohammadreza Alizadeh Aliabadi, Mohsen Karimi, Zahra Karimi, Mehrdad Soheili Fard,
Volume 20, Issue 0 (12-2024)
Abstract
Photoplethysmography (PPG) signals provide a non-invasive means of monitoring cardiovascular status during physical exercise; however, they are prone to noise, especially motion artifacts (MA). For specific telemedicine applications, compression is necessary for tasks such as PPG signal generation and secure data transmission. In this study, the investigation focused on determining whether it is better to perform compression before or after noise removal by applying a noise removal method and various compression methods. To achieve the aim, the study explored a subspace-based denoising method called "Maximum Uncorrelated PPG Denoising." Additionally, signal compression methods were examined in nine distinct steps. Compression quality is evaluated using various criteria, such as compression rate (CR) and Percentage Root Mean Square Difference (PRD). The results showed that regardless of the type of compression method, it is better not to remove noise before the compression process because doing so reduces CR and increases PRD.
