Bilateral filter

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

Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain

Abstract: This correspondence proposes an efficient algorithm for removing Gaussian noise from corrupted image by incorporating a wavelet-based trivariate shrinkage filter with a spatial-based joint bilateral filter. In the wavelet domain, the wavelet coefficients are modeled as trivariate Gaussian distribution, taking into account the statistical dependencies among intrascale wavelet coefficients, and then a trivariate shrinkage filter is derived by using the maximum a posteriori (MAP) estimator. Although wavelet-based methods are efficient in image denoising, they are prone to producing salient artifacts such as low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based algorithms output much higher quality denoising image with less artifacts. However, they are usually too computationally demanding. In order to reduce the computational cost, we develop an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain. This filter could suppress the noise while preserve image details with small computational cost. Extension to color image denoising is also presented. We compare our denoising algorithm with other denoising techniques in terms of PSNR and visual quality. The experimental results indicate that our algorithm is competitive with other denoising techniques.

Signal adaptive filtering method and signal adaptive filter for reducing blocking effect and ringing noise

Abstract: A signal adaptive filtering method is disclosed for reducing a blocking effect and ringing noise of an image data. A gradient of the image data is calculated for each pixel of the image data. Then, the gradient data of each pixel is compared with a global threshold value (T g ) which is determined based on a predetermined quantization step size (Q), and global edge map information of the pixel is generated. Meanwhile, the gradient data of each pixel is compared with a local threshold value (T n ) determined for each block having a predetermined size, and local edge map information of the pixel is generated. An OR operation is performed with respect to the global edge map information and the local edge map information to generate binary edge map information. Then, a predetermined sized filter window is applied to determine whether edges are present in the binary edge map information within the filter window. Afterwards, the image data pixel values of the corresponding filter window are filtered, pixel by pixel, by using predetermined first weighted values to generate a first new pixel value if it is determined that edges are not present. The image data pixel values within the corresponding filter window are filtered, pixel by pixel, by using predetermined second weighted values to generate a second new pixel value if it is determined that edges are present within the window. No filtering is performed if the pixel located at the center of the filter window represents an edge.

Channel smoothing: efficient robust smoothing of low-level signal features

Abstract: In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: it has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.

The trilateral filter for high contrast images and meshes

Abstract: We present a new, single-pass nonlinear filter for edge-preserving smoothing and visual detail removal for N dimensional signals in computer graphics, image processing and computer vision applications. Built from two modified forms of Tomasi and Manduchi's bilateral filter, the new "trilateral" filter smoothes signals towards a sharply-bounded, piecewise-linear approximation. Unlike bilateral filters or anisotropic diffusion methods that smooth towards piecewise constant solutions, the trilateral filter provides stronger noise reduction and better outlier rejection in high-gradient regions, and it mimics the edge-limited smoothing behavior of shock-forming PDEs by region finding with a fast min-max stack. Yet the trilateral filter requires only one user-set parameter, filters an input signal in a single pass, and does not use an iterative solver as required by most PDE methods. Like the bilateral filter, the trilateral filter easily extends to N-dimensional signals, yet it also offers better performance for many visual applications including appearance-preserving contrast reduction problems for digital photography and denoising polygonal meshes.

Visualization of Hyperspectral Images Using Bilateral Filtering

Abstract: This paper presents a new approach for hyperspectral image visualization. A bilateral filtering-based approach is presented for hyperspectral image fusion to generate an appropriate resultant image. The proposed approach retains even the minor details that exist in individual image bands, by exploiting the edge-preserving characteristics of a bilateral filter. It does not introduce visible artifacts in the fused image. A hierarchical fusion scheme has also been proposed for implementation purposes to accommodate a large number of hyperspectral image bands. The proposed scheme provides computational and storage efficiency without affecting the quality and performance of the fusion. It also facilitates the midband visualization of a subset of the hyperspectral image cube. Quantitative performance results are presented to indicate the effectiveness of the proposed method.