Block-matching 3D Filtering Research Articles

Denoising of two-photon fluorescence images with Block-Matching 3D filtering

Two-photon florescence imaging is widely used to perform morphological analysis of subcellular structures such as neuronal dendrites and spines, astrocytic processes etc. This method is also indispensable for functional analysis of cellular activity such as Ca2+ dynamics. Although spatial resolution of laser scanning two-photon system is greater than that of confocal or wide field microscope, it is still diffraction limited. In practice, the resolution of the system is more affected by its signal-to-noise ratio (SNR) than the diffraction limit. Thus, various approaches aiming to increase the SNR in two-photon imaging are desirable and can potentially save on building costly super-resolution imaging system. Here we analyze the statistics of noise in the two-photon florescence images of hippocampal astrocytes expressing genetically encoded Ca2+ sensor GCaMP2 and show that it can be reasonably well approximated using the same models which are used for describing noise in images acquired with digital cameras. This allows to use denoising methods available for wide field imaging on two-photon images. Particularly we demonstrate that the Block-Matching 3D (BM3D) filter can significantly improve the quality of two-photon fluorescence images so small details such as astrocytic processes can be easier identified. Moreover, denoising of the images with BM3D yields less noisy Ca2+ signals in astrocytes when denoising of the images with Gaussian filter.

Statistical properties of polarization image and despeckling method by multiresolution block-matching 3D filter

The theoretical and experimental investigations on the polarization imagery system of speckle statistical characteristics and speckle removing method are researched. A method to obtain two images encoded by polarization degree with a single measurement process is proposed. A theoretical model for polarization imagery system on Muller matrix is proposed. According to modern charge coupled device (CCD) imaging characteristics, speckles are divided into two kinds, namely small speckle and big speckle. Based on this model, a speckle reduction algorithm based on a dual-tree complex wavelet transform (DTCWT) and blockmatching 3D filter (BM3D) is proposed (DTBM3D). Original laser image data transformed by logarithmic compression is decomposed by DTCWT into approximation and detail subbands. Bilateral filtering is applied to the approximation subbands, and a suited BM3D filter is applied to the detail subbands. The despeckling results show that contrast improvement index and edge preserve index outperform those of traditional methods. The researches have important reference value in research of speckle noise level and removing speckle noise.

Fast blockwise SURE shrinkage for image denoising

In this paper, we investigate the shrinkage problem of image denoising for various methods under the additive white Gaussian noise (AWGN) model. Our main contribution is to derive the closed-form of the optimal shrinkage that minimizes the Stein's unbiased risk estimator (SURE) and thus allows direct blockwise shrinkage without additional optimizations. Simulation results show that the proposed method boosts the denoising performance for a variety of image denoising techniques including the moving average filter, the median filter, the wiener filter, the bilateral filter, the probabilistic non-local means, and the block matching 3D filter in terms of higher pixel signal noise ratio (PSNR) and structural similarity index (SSIM). We also case study the proposed shrinkage solution with respect to the classic NLM denoising, whose shrinkage solutions and equivalent forms have been widely researched, and further confirm its superiority. The proposed shrinkage solution can be used to improve arbitrary image denoising methods under the AWGN model, and it serves as a good remedy to save badly denoised images due to inappropriate parameters.

Combination of Target Detection and Block‐matching 3D Filter for Despeckling SAR Images

This Letter proposes a combination of target detection and block-matching 3D filter for despeckling SAR images. The proposed method is able to effectively preserve targets, such as the edges and dots of synthetic aperture radar (SAR) images, whilst removing noises. In the first step of our proposed method, based on despekling results of bilateral filtering and edge detection of a canny operator, some targets are detected and removed from an SAR image. The second step uses BM3D for denoising the targets-removed image. Finally, the removed targets are added to the despeckled targets-removed image and desirable results can be achieved.

Speckle reduction in optical coherence tomography images of human finger skin by wavelet modified BM3D filter

Speckle pattern, which is inherent in coherence imaging, degrades the visual quality of optical coherence tomography (OCT) images. Speckle noise reduction is an important post-processing step for OCT imaging. As multiplicative noise pattern, speckle noise is difficult to suppress efficiently. Current well know speckle removal techniques are not able to achieve the goals both on speckle suppression and on edge details preservation. To address this issue, a novel speckle noise reduction algorithm is proposed. The algorithm is based on block-matching 3D filter modified by morlet wavelet decomposition. Original OCT image data transformed by logarithmic compression is decomposed into 10 components by morlet wavelet for three levels. Each component is proposed by a suited BM3D filter and the output image is reconstructed by wavelet reverse transformation. Experiment to in vivo human index finger skin OCT images showed that the proposed algorithm achieves good performances in terms of signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, edge preservation coefficient, and CPU time compared to other recent high performance image speckle denoising algorithms. Visual comparisons also show that the proposed algorithm provides effective speckle noise suppression while preserving edge sharpness and improving morphological details visibility, such as different layers in finger skin OCT images.

Block matching 3D random noise filtering for absorption optical projection tomography

Absorption and emission optical projection tomography (OPT), alternatively referred to as optical computed tomography (optical-CT) and optical-emission computed tomography (optical-ECT), are recently developed three-dimensional imaging techniques with value for developmental biology and ex vivo gene expression studies. The techniques' principles are similar to the ones used for x-ray computed tomography and are based on the approximation of negligible light scattering in optically cleared samples. The optical clearing is achieved by a chemical procedure which aims at substituting the cellular fluids within the sample with a cell membranes' index matching solution. Once cleared the sample presents very low scattering and is then illuminated with a light collimated beam whose intensity is captured in transillumination mode by a CCD camera. Different projection images of the sample are subsequently obtained over a 360° full rotation, and a standard backprojection algorithm can be used in a similar fashion as for x-ray tomography in order to obtain absorption maps. Because not all biological samples present significant absorption contrast, it is not always possible to obtain projections with a good signal-to-noise ratio, a condition necessary to achieve high-quality tomographic reconstructions. Such is the case for example, for early stage's embryos. In this work we demonstrate how, through the use of a random noise removal algorithm, the image quality of the reconstructions can be considerably improved even when the noise is strongly present in the acquired projections. Specifically, we implemented a block matching 3D (BM3D) filter applying it separately on each acquired transillumination projection before performing a complete three-dimensional tomographical reconstruction. To test the efficiency of the adopted filtering scheme, a phantom and a real biological sample were processed. In both cases, the BM3D filter led to a signal-to-noise ratio increment of over 30 dB on severe noise-affected reconstructions revealing original—noise-hidden—image details. These results show the utility of the BM3D approach for OPT under typical conditions of very low light absorption, suggesting its implementation as an efficient alternative to other filtering schemes such as for example the median filter.