Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR).

Abstract: Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and prior-image-constrained-compressed-sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an average error of less than 1%.

Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images.

Abstract: A satellite image transmitted from satellite to the ground station is corrupted by different kinds of noises such as impulse noise, speckle noise and Gaussian noise. The traditional methods of denoising can remove the noise components but cannot preserve the quality of the image and lead to over-blurring of the edges in the image. To overcome these drawbacks, this paper develops an optimized bilateral filter for image denoising and preserving the edges using different nature inspired optimization algorithms which can effectively denoise the image without blurring the edges in the image. Denoising the image using a bilateral filter requires the decision of the control parameters so that the noise is removed and the edge details are preserved. With the help of optimization algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Adaptive Cuckoo Search (ACS), the control parameters in the bilateral filter are decided for optimal performance. It is observed that the proposed Adaptive Cuckoo Search based bilateral filter denoising gives better results in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Feature Similarity Index (FSIM), Entropy and CPU time in comparison to traditional methods such as Median filter and RGB spatial filter.

Measurement-based and scalable deblock filtering of image data

Abstract: In a deblocking operation, pixel values within a first block of pixels are compared, and pixel values in the first block are also compared to pixel values in a second block of pixels that is adjacent to the first block. Based on the results of the comparisons, a digital deblocking filter and a region of interest can be selected, where the region of interest identifies a number of pixels in the first block and a number of pixels in the second block to which the selected filter is to be applied.

Joint trilateral filtering for depth map super-resolution

Abstract: Depth map super-resolution is an emerging topic due to the increasing needs and applications using RGB-D sensors. Together with the color image, the corresponding range data provides additional information and makes visual analysis tasks more tractable. However, since the depth maps captured by such sensors are typically with limited resolution, it is preferable to enhance its resolution for improved recognition. In this paper, we present a novel joint trilateral filtering (JTF) algorithm for solving depth map super-resolution (SR) problems. Inspired by bilateral filtering, our JTF utilizes and preserves edge information from the associated high-resolution (HR) image by taking spatial and range information of local pixels. Our proposed further integrates local gradient information of the depth map when synthesizing its HR output, which alleviates textural artifacts like edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.