Among abundant image denoising methods proposed so far, the use of patch based algorithms have attracted a lot of attention from image processing community. Although these methods are very powerful in presentation of high quality results, the impact of human visual system (HVS) is ignored in sole of them. In this paper the human visual geometry is used in preparation of a new method for image denoising. Several image quality assessment (IQA) criteria, based on HVS, are used to confirm superiority of the proposed method in comparison with other state-of-the-art methods. In addition to denoising quality, the proposed method is fast as a result of dimensionality reduction.

Synthetic Aperture Radar (SAR) is widely used in different weather conditions for various applications such as mapping, remote sensing, urban, civil, and military monitoring. Recently, a new radar sensor called Video SAR (ViSAR) has been developed to capture sequential frames from moving objects for environmental monitoring applications such as image or video segmentation, classification and change detection. Same as SAR images, the major problem of ViSAR is the presence of speckle noise. In this paper, the performance of several image-based denoising methods is studied for de-speckling of ViSAR frames through “Frame-by-Frame”, “Averaging” and “3D” schemes. In “Frame-by-Frame” scheme, each video frame is denoised independently of the other frames, whereas, in “Averaging” scheme, the denoised images are averaged along a time window. In “3D” scheme, denoising is performed on 3D blocks in space-time (x-y-t) domain. In addition to these schemes, a novel extension on SAR-BM3D method, called ViSAR Incremental BM3D (ViSAR-IBM3D) approach is proposed for video denoising. The SAR-BM3D method performs denoising in two steps. At the first step, it uses wavelet denoising to primitively denoise the original image, in the next step, this image in combination with the original image are used to estimate the final denoised image. The main challenge of SAR-BM3D method is high time complexity especially for video frames. Here, in ViSAR-IBM3D, we benefit from the correlation between the frames of video and utilize the denoised images in previous frame to de-speckle the current frame. The proposed method can remarkably reduce the time complexity and improve preserving the details and the contrast of the denoised frames. The experimental results evaluated on real-world ViSAR video as well as video with simulated noises show that the proposed 3D filtering scheme and the proposed ViSAR-IBM3D method achieve better denoising performance than the other ones.

Two-dimensional (2D) adaptive filtering is a technique that can be applied to many image and signal processing applications. This paper extends the one-dimensional adaptive filter algorithms to 2D structure and the novel 2D adaptive filters are established. Based on this extension, the 2D variable step-size normalized least mean squares (2D-VSSNLMS), the 2D-VSS affine projection algorithms (2D-VSS-APA), the 2D set-membership NLMS (2D-SM-NLMS), the 2D-SM-APA, the 2D selective partial update NLMS (2DSPU- NLMS), and the 2D-SPU-APA are presented. In 2D-VSS adaptive filters, the stepsize changes during the adaptation which leads to improve the performance of the algorithms. In 2D-SM adaptive filter algorithms, the filter coefficients are not updated at each iteration. Therefore, the computational complexity is reduced. In 2D-SPU adaptive algorithms, the filter coefficients are partially updated which reduce the computational complexity. We demonstrate the good performance of the proposed algorithms thorough several simulation results in 2D adaptive noise cancellation (2D-ANC) for image denoising. The results are compared with the classical 2D adaptive filters such as 2D-LMS, 2D-NLMS, and 2D-APA.

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.

Magnetic Resonance Imaging (MRI) is a notable medical imaging technique that is based on Nuclear Magnetic Resonance (NMR). MRI is a safe imaging method with high contrast between soft tissues, which made it the most popular imaging technique in clinical applications. MR imagechr('39')s visual quality plays a vital role in medical diagnostics that can be severely corrupted by existing noise during the acquisition process. Therefore, the denoising of these images has great importance in medical applications. During the last decades, lots of MR denoising approaches from various groups of techniques have been proposed that can be classified into two general groups of acquisition-based noise reduction and post-acquisition denoising methods. The first groupchr('39')s approaches will add imaging time and led to a much time-consuming process. The second groupchr('39')s issues are its complicated mathematical equations required for image denoising, in which stochastic algorithms are usually required to solve these complex equations. This study aims to find an appropriate statical post-acquisition denoising MR imaging method based on the Bayesian technique. Finding the appropriate prior density function also has great importance since the Bayesian techniquechr('39')s performance is related to its prior density function. In this study, the uniform distribution has been applied as the prior density function. The prior uniform distribution function will reduce the Bayesian algorithm to its simplest possible state and lower computational complexity and time consumption. The proposed method can solve the numerical problems with an adequate timing process without complex algorithms and remove noise in less than 120 seconds on average in all cases. To quantitatively assess image improvement, we used the Structural Similarity Function (SSIM) in MATLAB. The similarity with this function shows an average improvement of more than 0. 1 in all images. Considering the results, it can be concluded that combining the uniform distribution function as a prior density function and the Bayesian algorithm can significantly reduce the imagechr('39')s noise without the time and computational cost.