3D Collaborative Filtering Research Articles

Breast ultrasound image despeckling using multi-filtering DFrFT and adaptive fast BM3D

Improving the quality of breast ultrasound images is of great significance for clinical diagnosis which can greatly boost the diagnostic accuracy of ultrasonography. However, due to the influence of ultrasound imaging principles and acquisition equipment, the collected ultrasound images naturally contain a large amount of speckle noise, which leads to a decrease in image quality and affects clinical diagnosis. To overcome this problem, we propose an improved denoising algorithm combining multi-filter DFrFT (Discrete Fractional Fourier Transform) and the adaptive fast BM3D (Block Matching and 3D collaborative filtering) method. Firstly, we provide the multi-filtering DFrFT method for preprocessing the original breast ultrasound image so as to remove some speckle noise early in fractional transformation domain. Based on the fractional frequency spectrum characteristics of breast ultrasound images, three types of filters are designed correspondingly in low, medium, and high frequency domains. And by integrating filtered images, the enhanced images are obtained which not only remove some speckle noise in background but also preserve the details of breast lesions. Secondly, for further enhancing the image quality on the basis of multi-filter DFrFT, we propose the adaptive fast BM3D method by introducing the DBSCAN-based super pixel segmentation to block matching process, which utilizes super pixel segmentation labels to provide a reference on how similar it is between target block and retrieval blocks. It reduces the number of blocks to be retrieved and make the matched blocks with more similar features. At last, the local noise parameter estimation is also adopted in the hard threshold filtering process of traditional BM3D algorithm to achieve local adaptive filtering and further improving the denoising effect. The synthetic data and real breast ultrasound data examples show that this combined method can improve the speckle suppression level and keep the fidelity of structure effectively without increasing time cost.

Seismic data denoising for complex structure using BM3D and local similarity

BM3D (Block Matching and 3D collaborative filtering) is an effective algorithm for random noise attenuation. It can create the denoised data with high signal-to-noise ratio. Different from the conventional denoising methods that apply just one set of fixed parameters to the whole section, BM3D denoises the digital image (seismic data) patch by patch. This method utilizes the redundant information on similar patches to obtain the final denoised results, thus getting the more obvious effect than the conventional ones. However, when applied to the seismic data with complex structure, it may do some harm to the details of structure and lower the fidelity of reflected signal due to the average effect and some threshold operations in the transform domain. To overcome this problem, we introduce the local similarity into BM3D method, which provides a standard on how similar it is between signal and noise. With this novel method (called BM3DL), we can recover the leaking signal energy from the noisy section and add it back to the denoised result. The synthetic models and real data examples show that this combined method can improve the signal-to-noise ratio of denoised section and keep the fidelity of structure effectively.

Improved Image Denoising Based on 3D Collaborative Filtering

Improved Image Denoising Based on 3D Collaborative Filtering

Modified BM3D algorithm for image denoising using nonlocal centralization prior

Block matching and 3D collaborative filtering (BM3D) has shown great power for image denoising. For grouped image blocks, this letter proposes to remove the 1D transform inter-blocks and introduce the nonlocal centralization prior to better utilize both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks. Three nonlocal shrinkage functions are developed under different norm restrictions of wavelet coefficients intra- and inter-blocks. Such shrinkage functions are verified to be competitive or better than the BM3D algorithm and other state-of-the-art denoising methods.