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The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.

CO2 storage needs economic business cases through cost-effective exploration and production and needs license-to-operate through public support. Re-interpretation and reprocessing of vintage geophysical data is a means to achieve cost-effective exploration whereas de-risking and conformance control of storage operations is a means to obtain public support. Seismic exploration should identify risk elements for CO2 storage such as the risk of leakage, risk of pressure build-ups or drops, unexpected increase or decrease of storage capacity and spill points to name a few. These risks elements are often caused by hidden features such as a failing overburden seal, closed or open faults in either reservoir or seal and high- or low-permeability streaks in the reservoir. We have investigated a seismic reprocessing workflow for imaging and de-risking CO2 storage reservoirs and seals. The workflow includes statics, demultiple, velocity modeling, Prestack Time Migration, high resolution sparse spike deconvolution and Non Local Means filtering. Non Local Means filtering increases signal to noise ratio while preserving edges and the sparse spike deconvolution produces results with superior vertical and lateral resolution. This workflow manages at low cost to considerably de-risk the CO2 storage reservoirs and seals by identifying previously hidden faults, seal-reservoir contacts and thin reservoir streaks. © 2018 European Association of Geoscientists and Engineers, EAGE. All rights reserved.

CO2 storage needs economic business cases through cost-effective exploration and production and needs license-to-operate through public support. Re-interpretation and reprocessing of vintage geophysical data is a means to achieve cost-effective exploration whereas de-risking and conformance control of storage operations is a means to obtain public support. Seismic exploration should identify risk elements for CO2 storage such as the risk of leakage, risk of pressure build-ups or drops, unexpected increase or decrease of storage capacity and spill points to name a few. These risks elements are often caused by hidden features such as a failing overburden seal, closed or open faults in either reservoir or seal and high- or low-permeability streaks in the reservoir. We have investigated a seismic reprocessing workflow for imaging and de-risking CO2 storage reservoirs and seals. The workflow includes statics, demultiple, velocity modeling, Prestack Time Migration, high resolution sparse spike deconvolution and Non Local Means filtering. Non Local Means filtering increases signal to noise ratio while preserving edges and the sparse spike deconvolution produces results with superior vertical and lateral resolution. This workflow manages at low cost to considerably de-risk the CO2 storage reservoirs and seals by identifying previously hidden faults, seal-reservoir contacts and thin reservoir streaks. © 2018 European Association of Geoscientists and Engineers, EAGE. All rights reserved.

Background We investigate the use of different denoising filters on low signal-to-noise ratio cardiac images of the rat heart acquired with a birdcage volume coil at 7T. Accuracy and variability of cardiac function parameters were measured from manual segmentation of rat heart images with and without filtering. Methods Ten rats were studied using a 7T Varian system. End-diastolic and end-systolic volumes, ejection fraction and left ventricle mass (LVM) were calculated from manual segmentation by two experts on cine-FLASH short-axis slices covering the left ventricle. Series were denoised with an anisotropic diffusion filter, a whole variation regularization or an optimized Rician non-local means (ORNLM) filtering technique. The effect of the different filters was evaluated by the calculation of signal-to-noise (SNR) and contrast-to-noise (CNR) ratios, followed by a study of intra- and inter-expert variability of the measurement of physiological parameters. The calculated LVM was compared to the LVM obtained by weighing the heart ex vivo. Results The SNR and the CNR increased after application of the different filters. The performance of the ORNLM filter was superior for all the parameters of the cardiac function, as judged from the inter- and intra-observer variabilities. Moreover, this filtering technique resulted in the lowest variability in the LVM evaluation. Conclusions In cardiac MRI of rats, filtering is an interesting alternative that yields better contrast between myocardium and surrounding tissues and the ORNLM filter provided the largest improvements.

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels.

Background Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.

Date of Conference: 16-19 May 2016 Conference Name: IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016 Diffusion-weighted imaging (DWI) suffers from low SNR when compared to regular MRI images. To attain reasonable SNR levels with high spatial resolution, multiple acquisitions of each slice have to be acquired. These repetitions are then averaged, with the goal of increasing the SNR. However, subject motion during diffusion-sensitizing gradients creates varying phases between repeated acquisitions. When direct complex averaging is performed, these phase offsets can cause unpredictable phase cancellations and local signal drops. Here, we propose a technique that simultaneously corrects phase and performs a non-local means filtering to reduce noise. The success of the proposed technique is demonstrated in vivo at 3T with DWI of the cervical spinal cord.

International audience; Although the nonlocal means (NLM) algorithm takes a significant step forward in image filtering field, it suffers from a high computational complexity. To deal with this drawback, this paper proposes an acceleration strategy based on a correlation operation. Instead of per-pixel processing, this approach performs a simultaneous calculation of all the image pixels with the help of correlation operators. Complexity analysis and experimental results are reported and show the advantage of the proposed algorithm in terms of computation and time cost. © 2016 Junfeng Zhang et al.

In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers, thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing.