Switching Bilateral Filter Research Articles

De-Speckling Breast Cancer Ultrasound Images Using a Rotationally Invariant Block Matching Based Non-Local Means (RIBM-NLM) Method.

The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues on ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages in image processing pipeline, such as edge detec-tion, segmentation, feature extraction, and classification. Previous studies have formulated vari-ous speckle reduction methods in ultrasound images; however, these methods suffer from being unable to retain finer edge details and require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotational invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method has been demonstrated by com-paring our results with three established de-speckling techniques, the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF) on 250 images from public dataset and 6 images from private dataset. Evaluation metrics, including Self-Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) were utilized to measure performance. With the proposed method, we were able to record average SSIM of 0.8915, PSNR of 65.97, MSE of 0.014, RMSE of 0.119, and computational speed of 82 seconds at noise variance of 20dB using the public dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved av-erage SSIM of 0.83, PSNR of 66.26, MSE of 0.015, RMSE of 0.124, and computational speed of 83 seconds at noise variance of 20dB using the private dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF.

A novel switching bilateral filtering algorithm for depth map

n this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.

Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal

Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block‐matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

Rayleigh-maximum-likelihood bilateral filter for ultrasound image enhancement

BackgroundUltrasound imaging plays an important role in computer diagnosis since it is non-invasive and cost-effective. However, ultrasound images are inevitably contaminated by noise and speckle during acquisition. Noise and speckle directly impact the physician to interpret the images and decrease the accuracy in clinical diagnosis. Denoising method is an important component to enhance the quality of ultrasound images; however, several limitations discourage the results because current denoising methods can remove noise while ignoring the statistical characteristics of speckle and thus undermining the effectiveness of despeckling, or vice versa. In addition, most existing algorithms do not identify noise, speckle or edge before removing noise or speckle, and thus they reduce noise and speckle while blurring edge details. Therefore, it is a challenging issue for the traditional methods to effectively remove noise and speckle in ultrasound images while preserving edge details.MethodsTo overcome the above-mentioned limitations, a novel method, called Rayleigh-maximum-likelihood switching bilateral filter (RSBF) is proposed to enhance ultrasound images by two steps: noise, speckle and edge detection followed by filtering. Firstly, a sorted quadrant median vector scheme is utilized to calculate the reference median in a filtering window in comparison with the central pixel to classify the target pixel as noise, speckle or noise-free. Subsequently, the noise is removed by a bilateral filter and the speckle is suppressed by a Rayleigh-maximum-likelihood filter while the noise-free pixels are kept unchanged. To quantitatively evaluate the performance of the proposed method, synthetic ultrasound images contaminated by speckle are simulated by using the speckle model that is subjected to Rayleigh distribution. Thereafter, the corrupted synthetic images are generated by the original image multiplied with the Rayleigh distributed speckle of various signal to noise ratio (SNR) levels and added with Gaussian distributed noise. Meanwhile clinical breast ultrasound images are used to visually evaluate the effectiveness of the method. To examine the performance, comparison tests between the proposed RSBF and six state-of-the-art methods for ultrasound speckle removal are performed on simulated ultrasound images with various noise and speckle levels.ResultsThe results of the proposed RSBF are satisfying since the Gaussian noise and the Rayleigh speckle are greatly suppressed. The proposed method can improve the SNRs of the enhanced images to nearly 15 and 13 dB compared with images corrupted by speckle as well as images contaminated by speckle and noise under various SNR levels, respectively. The RSBF is effective in enhancing edge while smoothing the speckle and noise in clinical ultrasound images. In the comparison experiments, the proposed method demonstrates its superiority in accuracy and robustness for denoising and edge preserving under various levels of noise and speckle in terms of visual quality as well as numeric metrics, such as peak signal to noise ratio, SNR and root mean squared error.ConclusionsThe experimental results show that the proposed method is effective for removing the speckle and the background noise in ultrasound images. The main reason is that it performs a “detect and replace” two-step mechanism. The advantages of the proposed RBSF lie in two aspects. Firstly, each central pixel is classified as noise, speckle or noise-free texture according to the absolute difference between the target pixel and the reference median. Subsequently, the Rayleigh-maximum-likelihood filter and the bilateral filter are switched to eliminate speckle and noise, respectively, while the noise-free pixels are unaltered. Therefore, it is implemented with better accuracy and robustness than the traditional methods. Generally, these traits declare that the proposed RSBF would have significant clinical application.

An Optimised Fuzzy Approach to Remove Mixed Noise from Images

Mixed noises can be defined as a combination of different types of noises acting on a single carrier. There has been a mention of various mechanisms used to restore images corrupted with mixed noise in the past. This paper proposes a simple method based on fuzzy set theory and Bilateral Filter to remove mixed noises and compares it with previously mentioned techniques such as: Vector Median Filter(VMF), Vector Direction Filter (VDF), Fuzzy Peer Group Averaging (FPGA), Fuzzy Vector Median Filter (FVMF), Bilateral Filter (BF), Adaptive Bilateral Filter (ABF), Switching Bilateral Filter (SBF), Joint Bilateral Filter (JBF), and Trilateral Filter (TF) on the basis of performance metrics such as Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), Mean Square Error (MSE) and Normalised Colour Difference (NCD). For the purpose of a detailed analysis, the performance of each method is evaluated by varying the image size and the noise density by implementing them in MATLAB-09. The mixed noise used in this paper is a combination of three noise i.e. poisson, impulse and Gaussian noise. The simulation and result shows that the proposed method provides better PSNR and hence better image quality than almost all the methods mentioned above.

Comparison of Noise Removal Techniques Using Bilateral Filter

This paper compares the standard Bilateral Filter and its variants such as Modified Bilateral Filter(MBF), Joint Bilateral Filter(JBF), Fuzzy Bilateral Filter(FBF) and Switching Bilateral Filter(SBF). For comparison purpose various performance matrices such as: Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), Mean Absolute Error(MAE), Normalized Color Difference(NCD), Perceptual Quality and Time complexity are used . All the techniques are implemented using simulation in MATLAB-9. It is found that the standard BF is the best technique to remove Gaussian noise from images with high PSNR value. However when the image is corrupted with mixed Gaussian and impulse noise, SBF gives the best results of PSNR value, MSE value, MAE value , NCD value and a good picture quality of de-noised image.

An extension to switching bilateral filter for mixed noise removal from colour image

Noise elimination is a fundamental operation of image processing in order to enhance, analyse and interpret the important information in an image. Several types of noise have been studied but it is found that more than one noise type contaminate the images. Gaussian and impulse noises are the ones that are more often introduced as mixed noise. Despite the existence of many filtering solutions to reduce the different noise types separately, only a few methods to process mixed noise have been proposed. In addition these methods work for grey scale images. This paper proposes a technique for removing mixed noise from colour image while preserving edges at same time by modifying the Switching Bilateral Filter (SBF). The implementation is done in MATLAB-7 and compared with previous ones. It is found that the proposed technique provides better results for densely corrupted images. Moreover, the proposed technique provides less colour blurriness.

A Survey of Various Bilateral Filtering Techniques

Noise not only deteriorates image quality but also may result in loss of important information hidden in images (i.e. medical applications). Various types of noises are available in literature such as Gaussian noise, impulse noise, mixed noise etc. In order to remove these noises from images, bilateral filters and its variants are used. This paper surveys the impact of these techniques using several performance metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Mean Absolute Error (MAE) and Time Complexity. For this purpose a simulator is designed in MATLAB to implement these techniques. The results show that Joint Bilateral Filter (JBF) technique is the best technique for removing Gaussian noise, Modified Double Bilateral Filter (MDBF) technique provides good results for removing impulse noise and Switching Bilateral Filter (SBF) technique work well for mixed noise as seen from results.

A Comparative Analysis of Impulse Noise Removal Techniques on Gray Scale Images

Various kinds of images and pictures are required as sources of information for analysis and interpretation. When an image is converted from one form to another such as scanning, transmitting, digitizing, storing etc., degradation occurs to the output image. Hence, the output image needs to be enhanced in order to be better analyzed. Denoising is the one of the pre processing technique in digital image processing. This paper investigates the performance of four denoising methods for removing the High Density Impulse Noise. They are Adaptive Bilateral Filter (ABF), Fuzzy Peer Group Filter (FPGF), Switching Bilateral Filter (SBF), and Boundary Discriminative Noise Detection Filter (BDND).The performance of the above four filters is compared by using five performance metrics. They are PeakSignal-to-Noise-Ratio, Mean Square Error and Root Mean Square Error. The Experimental results show that the BDND filter based denoising method performs well than the other three methods.

Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.