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Image speckle noise denoising by a multi-layer fusion enhancement method based on block matching and 3d filtering

IMAGING SCIENCE JOURNAL 2019年第4期67卷 224-235页

作者： Huang, Shuo Zhou, Ping Shi, Hao Sun, Yu Wan, Suiren Southeast Univ Sch Biol Sci & Med Engn State Key Lab Bioelect Int Lab Childrens Med Imaging Res Nanjing 210096 Jiangsu Peoples R China Shanghai United Imaging Healthcare Co Ltd Shanghai Peoples R China Univ Birmingham Inst Canc & Genom Sci Birmingham W Midlands England

In order to improve speckle noise denoising of block matching and 3d filtering (BM3d) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3d) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3d denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3d method can achieve better visual effects than BM3d method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5dB. The MLFE-BM3d method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.

关键词： Image denoising speckle noise block matching and 3d filtering frequency-domain layering multi-layer fusion enhancement

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Image speckle noise denoising by a multi-layer fusion enhancement method based on block matching and 3d filtering

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The Journal of Photographic Science 2019年第4期67卷 224-235页

ABSTRACTABSTRACTIn order to improve speckle noise denoising of block matching and 3d filtering (BM3d) method, an image frequency-domain multi-layer fusion enhancement method (MLFE-BM3d) based on nonsubsampled contourlet transform (NSCT) has been proposed. The method designs an NSCT hard threshold denoising enhancement to preprocess the image, then uses fusion enhancement in NSCT domain to fuse the preliminary estimation results of images before and after the NSCT hard threshold denoising, finally, BM3d denoising is carried out with the fused image to obtain the final denoising result. Experiments on natural images and medical ultrasound images show that MLFE-BM3d method can achieve better visual effects than BM3d method, the peak signal to noise ratio (PSNR) of the denoised image is increased by 0.5 dB. The MLFE-BM3d method can improve the denoising effect of speckle noise in the texture region, and still maintain a good denoising effect in the smooth region of the image.

关键词： Image denoising speckle noise block matching and 3d filtering frequency-domain layering multi-layer fusion enhancement

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Improved image denoising via RAISR with fewer filters

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Computational Visual Media 2021年第4期7卷 499-511页

作者： Theingi Zin Yusuke Nakahara Takuro Yamaguchi Masaaki Ikehara Department of Electronics and Electrical Engineering Keio UniversityYokohama-shi223-8522Japan

In recent years,accurate Gaussian noise removal has attracted considerable attention for mobile applications,as in smart *** conventional denoising methods have the potential ability to improve denoising performance with no additional ***,we propose a rapid post-processing method for Gaussian noise removal in this *** matching and 3d filtering and weighted nuclear norm minimization are utilized to suppress *** these nonlocal image denoising methods have quantitatively high performance,some fine image details are lacking due to the loss of high frequency *** tackle this problem,an improvement to the pioneering RAISR approach(rapid and accurate image super-resolution),is applied to rapidly post-process the denoised *** gives performance comparable to state-of-the-art super-resolution techniques at low computational cost,preserving important image structures *** modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements:geometric conversion and reduction of the strength *** addition,following RAISR,the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free *** results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods,efficiently and with low memory requirements.

关键词： block matching and 3d filtering weighted nuclear norm minimization super-resolution geometric conversion census transform

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COMPARISON OF SATELLITE IMAGE dENOISING TECHNIQUES IN SPATIAL ANd FREQUENCY dOMAINS 24

COMPARISON OF SATELLITE IMAGE DENOISING TECHNIQUES IN SPATIA...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Oguzhanoglu, S. Kapucuoglu, I Sunar, F. ITU Civil Engn Fac TR-80626 Maslak Turkey

In recent years, remote sensing images have been used for many different applications that require visual analysis and interpretation. In this paper, reducing/removing noise is the basic approach, as it causes loss of information and therefore affects the accuracy of the analyses. Within the scope of the study, two different test areas of land cover/use were applied to examine the effects of noise on optical satellite images. In this context, Landsat 8 and Sentinel 2 satellites were used to study the effects of denoising methods on different spatial resolutions. due to the lack of raw images of the selected satellites, two different types of noise (i.e. Gaussian and Stripe) were added to the images. In this context, four different denoising methods were compared by using conventional filter techniques commonly used in the spatial domain, while also different methods that used different threshold values in the frequency domain. The first approach is Median, block matching and 3d filtering methods in the spatial domain, applications that depend mainly on the neighborhood relationship of pixels in the image. The second approach is wavelet-based Contourlet and Curvelet methods in the frequency domain. The quality analysis of denoised images were evaluated as qualitative (statistical methods Peak Signal to Noise Ratio, Mean Square Error, standard deviation, min/max value), and quantitative. Finally, Curvelet hard thresholding transform was the selected method as the best algorithm after quality analysis additionally, the method also effectively preserves edges in homogeneous test area and other fine details in the heterogeneous test area.

关键词： Noise denoising Optical Satellite Images Wavelet-based Contourlet Transform Curvelet Transform block matching and 3d filtering Median Filter

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Noise reduction of shot-noise-dominated hyperspectral imagery by combining PCA with existing denoising methods

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION ANd INFORMATION PROCESSING 2021年第6期19卷 2150028-2150028页

作者： Chen, Guang Yi Krzyzak, Adam Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

In this paper, we revisit the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous work combined PCA with wavelet shrinkage and particularly good denoising results has been achieved. We debate that any denoising methods can be used to replace wavelet shrinkage in our PCA+wavelet shrinkage algorithm. The major difference between this work and our previous PCA-based denoising method is that we consider a mixture of Gaussian and shot noise in this work whereas our previous methods studied Gaussian white noise alone. In addition, we retain k(1) (k(1) < d) PCA output components in our forward PCA transform in this paper whereas we keep all PCA output components (k(1) < d) in our previous works. The d above is the number of spectral bands in the original hyperspectral imagery data cube. In addition, PCA is much better than nonlinear PCA for hyperspectral imagery denoising when Gaussian white noise and shot noise are introduced as demonstrated in this paper. Extensive experiments demonstrate that the method proposed in this paper outperforms the existing methods significantly in terms of signal-to-noise ratio for two testing hyperspectral imagery data cubes.

关键词： Hyperspectral imagery denoising Poisson shot noise Gaussian noise principal component analysis block matching and 3d filtering

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Sequential Regularized MLEM Reconstruction Method for Incomplete Sinograms

Sequential Regularized MLEM Reconstruction Method for Incomp...

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IEEE Nuclear Science Symposium/Medical Imaging Conference (NSS/MIC)/18th International Workshop on Room-Temperature Semiconductor X-Ray and Gamma-Ray detectors

作者： Tuna, Uygar Pepe, Antonietta Ruotsalainen, Ulla Tampere Univ Technol Dept Signal Proc BioMediTech FIN-33101 Tampere Finland

ISBN: (纸本)9781467301206

In this study, we investigate the capability of the well-known maximum likelihood expectation maximization (MLEM) method in handling the incomplete sinogram reconstruction. MLEM method is known to be able to deal with the missing parts via system matrix modeling. We propose sequentially applied regularized MLEM method for the image reconstruction from the incomplete sinograms. Median filtering (MEd), L-filtering (L) and block matching 3d (BM3d) filtering were employed as spatial domain regularizers, separately. The MLEM method was applied sequentially by decreasing gradually the amount of the regularization when the image update between the consecutive iterations was negligible. The sequential application of the MLEM method is used to compensate for the blurring caused by the regularization filters. In order to test the proposed approach thoroughly, we constructed wide test dataset consisting of three sparse sinograms (by using 25%, 10% and 5.5% of the sinogram data), the ECAT HRRT type sinogram (81.2% of the sinogram data), ClearPET type sinogram (88.3% of the sinogram data), AX-PET type sinogram (56% of the sinogram data) and a hypothetical checkerboard type sinogram (50% of the sinogram data) of the Shepp-Logan phantom. The constructed incomplete sinograms were contaminated with the Poisson noise. The incomplete sinograms were reconstructed by using filtered backprojection, MLEM without filtering, MLEM with MEd, MLEM with L and MLEM with BM3d filtering. We evaluated the approach visually and with the quantitative normalized mean square error (NMSE). The results showed that MLEM without filtering cannot correct the artifacts related to the missing sinogram bins for some of the generated incomplete sinograms. On the other hand, MLEM method with spatial regularization filter was able to provide successfully reconstructed images for all cases. Quantitative NMSE values were in parallel with the visual impression of the reconstructed images.

关键词： block matching and 3d filtering L filter median filtering missing sinogram bins quantitative evaluation region of interest regularized statistical reconstruction method sparse sinogram

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