Speckle Noise Reduction in Medical Ultrasound Images

Ultrasound imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. In this paper, we propose a denoising approach which combines logarithmic transformation and a non linear diffusion tensor. Since speckle noise is multiplicative and nonwhite process, the logarithmic transformation is a reasonable choice to convert signaldependent or pure multiplicative noise to an additive one. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image. To illustrate the effective performance of our algorithm, we present some experimental results on synthetically and real echographic images

Performance Analysis of Intensity Averaging By Anisotropic Diffusion Method for MRI Denoising Corrupted By Random Noise

The two parameters which plays important role in MRI(magnetic resonance imaging),acquired by various imaging modalities are Feature extraction and object recognition. These operations will become difficult if the images are corrupted with noise. Noise in MR image is always random type of noise. This noise will change the value of amplitude and phase of each pixel in MR image. Due to this, MR image gets corrupted and we cannot make perfect diagnostic for a body. So noise removal is essential task for perfect diagnostic. There are different approaches for noise reduction, each of which has its own advantages and limitation. MRI denoising is a difficult task task as fine details in medical image containing diagnostic information should not be removed during noise removal process. In this paper, we are representing an algorithm for MRI denoising in which we are using iterations and Gaussian blurring for amplitude reconstruction and image fusion,anisotropic diffusion and FFT for phase reconstruction. We are using the PSNR(Peak signal to noise ration),MSE(Mean square error) and RMSE(Root mean square error) as performance matrices to measure the quality of denoised MRI. The final result shows that this method is effectively removing the noise while preserving the edge and fine information in the images

Q-switched fiber laser based on CdS quantum dots as a saturable absorber

In this work, a Q-switched fiber laser is demonstrated using quantum dots (QDs) cadmium sulfide (CdS) as a saturable absorber (SA) in an erbium-doped fiber laser (EDFL) system. The QD CdS is synthesized via the microwave hydrothermal assisted method and embedded into polyvinyl alcohol (PVA). The QD CdS/PVA matrix film is sandwiched in between two fiber ferrules by a fiber adapter. The generation of Q-switched fiber laser having a repetition rate, a pulse width, and a peak-topeak pulse duration of 75.19 kHz, 1.27 μs, and 13.32 μs, respectively. The maximum output power of 3.82 mW and maximum pulse energy of 50.8 nJ are obtained at the maximum pump power of 145.9 mW. The proposed design may add to the alternative material of Q-switched fiber laser generation, which gives a high stability output performance by using quantum dots material as a saturable absorber

Despeckling Of Synthetic Aperture Radar Images Using Shearlet Transform

Synthetic Aperture Radar (SAR) is widely used for producing high quality imaging of Earth sur- face due to its capability of image acquisition in all- weather conditions. However, one limitation of SAR image is that image textures and fine details are usually contaminated with multiplicative granular noise named as speckle noise. This paper presents a speckle reduc- tion technique for SAR images based on statistical mod- elling of detail band shearlet coefficients (SC) in ho- momorphic environment. Modelling of SC correspond- ing to noiseless SAR image are carried out as Nor- mal Inverse Gaussian (NIG) distribution while speckle noise SC are modelled as Gaussian distribution. These SC are segmented as heterogeneous, strongly hetero- geneous and homogeneous regions depending upon the local statistics of images. Then maximum a posteri- ori (MAP) estimation is employed over SC that belong to homogenous and heterogenous region category. The performance of proposed method is compared with seven other methods based on objective and subjective quality measures. PSNR and SSIM metrics are used for objec- tive assessment of synthetic images and ENL metric is used for real SAR images. Subjective assessment is carried out by visualizing denoised images obtained from various methods. The comparative result analy- sis shows that for the proposed method, higher values of PSNR i.e. 26.08 dB, 25.39 dB and 23.82 dB and SSIM i.e. 0.81, 0.69 and 0.61 are obtained for Barbara im- age at noise variances 0.04, 0.1 and 0.15, respectively as compared to other methods. For other images also results obtained for proposed method are at higher side. Also, ENL for real SAR images show highest average value of 125.91 79.05. Hence, the proposed method sig- nifies its potential in comparison to other seven existing image denoising methods in terms of speckle denoising and edge preservation