All Medical pix incorporate some visible and unvisible random disturbances. These are termed as noises. It represents undesirable statistics that deteriorates the image quality. There are different types of noises in photographs and it can be efficaciously eliminated through the usage of filters. So in widespread filters are used to decrease the noise with the aid of smoothing and additionally used to beautify the edges. Filters are usually been labeled as linear and non linear filters. Linear filters are incapable of smoothing besides concurrently blurring the edges. In our context we are going to enforce a median 2 filter, Wiener filter, imadjsut filter, both tophat and bothat filters(morphology) for filtering 2D X-ray images. These combination of filters yields the best part in the preprocessing techniques. It gives a good PSNR and MSE values.

Filters Used in X-Ray Chest Images for Initial Stage Tuberculosis Detection

In this paper we focus on speckle noise removal. Previously, variational models have been proposed to remove the multiplicative speckle noise. In general, the variational models require a significant amount of run time to converge, and need to set the proper tuning parameter values to achieve optimal noise reduction results. In this paper, we present a local polynomial regression model for speckle noise removal. Our regression model is fast, does not need to be trained on a set of images, does not rely on tuning parameters, and is capable of performing fast speckle noise removal on high resolution images. We have conducted extensive experiments to evaluate our model performance. Our polynomial regression filter outperformed popular noise removal algorithms.

Fast local polynomial regression approach for speckle noise removal

Recently, denoising of Synthetic Aperture Radar (SAR) images has gained particular attention. SAR image is usually affected by speckle noise. In this paper a new method for speckle noise reduction of SAR images using shearlet transform (ST) is introduced. ST could significantly remove the Gaussian noise therefore in the proposed method first, noisy images are converted to a domain which type of noise is Gaussian using homomorphic transform (HT). Second, 2D shear-let is applied to the data. Third, the hard thresholding is used in order to denoise the shearlet coefficients. Finally reconstructed denoised images are obtained by applying the inverse shearlet and homomorphic transforms. The proposed method (ST-HT) is compared with state of art denoising algorithms on SAR images. Obtained results show the superiority of the proposed approach.

SAR image denoising using homomorphic and shearlet transforms

3D synthetic aperture radar (SAR) is one of the hotspots in microwave imaging researches, which can implement the high 3D resolution and visually present 3D radar images of measured objects. Because the data amount and complexity of 3D SAR is much higher than traditional 2D SAR, it is required to study how to acquire the valid information and filter the invalid information (such as clutter and noise) precisely and quickly. This paper presents an adaptive filtering algorithm for 3D SAR data based on the dynamic Gaussian threshold (DGT). According to a given focal point for 3D SAR imaging, a group of dynamic Gaussian functions is introduced to filter the invalid information adaptively. Since there is the parallel relationship between the proposed DGT filtering algorithm and 3D SAR imaging algorithms, the proposed algorithm can satisfy the requirement for precision and efficiency simultaneously, without the post-processing of 3D SAR results. The actual experiment results show that the proposed algorithm can improve the signal-to-clutter and noise ratio (SCNR) about 40 dB for the point target and about 30 dB for the surface target.

Adaptive Filtering for 3D SAR Data based on Dynamic Gaussian Threshold

The error between true colours of a point in a scene relates to real world, and the pixel obtained from an image sensor is known as noise. Various noises were observed, and different filtration and sub-band decomposition techniques were also achieved most success rate in reducing the noise rate. But this leads to harshness on the image pixels they were tuned to more sharp than they need. Now a novel approach discussed in next sections which helps in learning minimization of sharp noise and increases the smooth characteristics of the noisy images. The noise reduction process is carried out by an iterative decomposition method and very much dependent on intrinsic mode function (IMF) and is known as variable mode decomposition (VMD). The database comprised with medical images and synthetic aperture radar images, and the experiments were done on SAR and medical images and shown in results section. This also helps in optimising the direction method of multipliers approach.

Smoothing and Noise Reduction in Images Using Variable Mode Decomposition

One of the inherent characteristics of radar images is the presence of speckle noise. Speckle appears as a grainy texture in the image and highly reduces the image quality. Therefore, it is desirable to reduce speckle, prior to any image interpretation. With regard to the importance of synthetic aperture radar (SAR) images, a lot of efforts have already been made to remove speckle noise from radar images, and accordingly famous filters have been introduced, each with their special advantages and disadvantages. In this paper, we examine five methods like the ones in the field of space and frequency domain. we will compare five different approaches: Wavelet Thresholding methods, anisotropic diffusion and speckle reducing anisotropic diffusion, also we suggest a method for reducing speckle of synthetic aperture radar images which is in fact a combination of hybrid mean-median filter and the method of speckle reducing anisotropic diffusion. The results indicate that the performance of our proposed method, based on criteria such as PSNR, improving SNR, standard protect the edge (β), in almost all cases is better the other compared methods; and it also offers more desirable results from the point of visual quality.

Masume Rahimi

Papers

A new hybrid algorithm for speckle noise reduction of SAR images based on mean-median filter and SRAD method