Topic
Bilateral filter
About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.
Papers published on a yearly basis
Papers
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TL;DR: An improved de-speckling method for medical ultrasound images is proposed, based on the wavelet transformation and fast bilateral filter, which has better reduction performance than other methods but also can preserve image details such as the edge of lesions.
47 citations
20 Nov 2011
TL;DR: Experiments showed the results produced from the proposed adaptive guided image filtering (AGF) are superior to those produced from unsharp masking-based techniques and comparable to ABF filtered output.
Abstract: Sharpness enhancement and noise reduction play crucial roles in computer vision and image processing. The problem is to enhance the appearance and reduce the noise of the digital images without causing halo artifacts. In this paper, we propose an adaptive guided image filtering (AGF) able to perform halo-free edge slope enhancement and noise reduction simulaneously. The proposed method is developed based on guided image filtering (GIF) and the shift-variant technique, part of adaptive bilateral filtering (ABF). Experiments showed the results produced from our method are superior to those produced from unsharp masking-based techniques and comparable to ABF filtered output. Our proposed AGF outperforms ABF in terms of computational complexity. It is implemented using a fast and exact linear-time algorithm.
47 citations
Patent•
23 Jan 2002TL;DR: In this article, a spatio-temporal filter unit (100) is proposed, which integrates spatial and implicit motion-compensated temporal noise reduction in one filter, where no motion vectors are required.
Abstract: Noise reduction is an important feature in consumer television. This is realized by spatial, temporal or spatio-temporal filters. Spatial filters require pixels from within one image, while temporal filters require samples from two or more successive images. The spatio-temporal filter unit (100) according to this invention integrates spatial and implicit motion-compensated temporal noise reduction in one filter. For the motion compensation no motion vectors are required. The spatio-temporal filter unit (100) is provided with a sigma filter (112) comprising one filter kernel (107) designed to operate on the pixels from both a current image and from the output of the spatio-temporal filter unit, being a temporally recursive filtered image. The operation of the spatio-temporal filter unit (100) can be adjusted by varying the thresholds of the sigma filter (112) and the selection of pixels. The adjustments can be controlled by a motion estimator (222), a motion detector (224) and a noise estimator (220).
46 citations
TL;DR: The multi-modal brain images dataset (BraTs 2012) was used and achieves the dice overlap score of 88% for the whole tumour area localization, which is similar to the declared score in MICCAI BraTS challenge.
46 citations
01 Jun 2016
TL;DR: The experimental results showed that the BF with parameters proposed by the authors showed a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images.
Abstract: This is the first GA based-optimization study to find optimal parameters of bilateral filter.Both the simulated and clinical brain MR images were used for Rician noise removal.The preservation of edges and removal of noise were investigated for different noise levels.A better performance in computation time of our approach was observed.The quality of the denoised images with the proposed parameters was validated using quantitative metrics. Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF were optimized using genetic algorithm (GA). First, the Rician noise with different variances (?=10, 20, 30) was added to simulated T1-weighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size 3×3, 5×5, 7×7, 11×11, and 21×21, spatial sigma 0.1-10 and intensity sigma 1-60. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters, we investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF, we compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results showed that the BF with parameters proposed by us showed a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection.
46 citations