Bilateral filter

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

Compressive Bilateral Filtering

Abstract: This paper presents an efficient constant-time bilateral filter that produces a near-optimal performance tradeoff between approximate accuracy and computational complexity without any complicated parameter adjustment, called a compressive bilateral filter (CBLF). The constant-time means that the computational complexity is independent of its filter window size. Although many existing constant-time bilateral filters have been proposed step-by-step to pursue a more efficient performance tradeoff, they have less focused on the optimal tradeoff for their own frameworks. It is important to discuss this question, because it can reveal whether or not a constant-time algorithm still has plenty room for improvements of performance tradeoff. This paper tackles the question from a viewpoint of compressibility and highlights the fact that state-of-the-art algorithms have not yet touched the optimal tradeoff. The CBLF achieves a near-optimal performance tradeoff by two key ideas: 1) an approximate Gaussian range kernel through Fourier analysis and 2) a period length optimization. Experiments demonstrate that the CBLF significantly outperforms state-of-the-art algorithms in terms of approximate accuracy, computational complexity, and usability.

The Guided Bilateral Filter: When the Joint/Cross Bilateral Filter Becomes Robust

Abstract: The bilateral filter and its variants, such as the joint/cross bilateral filter, are well-known edge-preserving image smoothing tools used in many applications. The reason of this success is its simple definition and the possibility of many adaptations. The bilateral filter is known to be related to robust estimation. This link is lost by the ad hoc introduction of the guide image in the joint/cross bilateral filter. We here propose a new way to derive the joint/cross bilateral filter as a particular case of a more generic filter, which we name the guided bilateral filter. This new filter is iterative, generic, inherits the robustness properties of the robust bilateral filter, and uses a guide image. The link with robust estimation allows us to relate the filter parameters with the statistics of input images. A scheme based on graduated nonconvexity is proposed, which allows converging to an interesting local minimum even when the cost function is nonconvex. With this scheme, the guided bilateral filter can handle non-Gaussian noise on the image to be filtered. A complementary scheme is also proposed to handle non-Gaussian noise on the guide image even if both are strongly correlated. This allows the guided bilateral filter to handle situations with more noise than the joint/cross bilateral filter can work with and leads to high peak signal-to-noise ratio values as shown experimentally.

SNR Enhancement in Phase-Sensitive OTDR with Adaptive 2-D Bilateral Filtering Algorithm

Abstract: An adaptive image restoration algorithm based on 2-D bilateral filtering has been proposed to enhance the signal-to-noise ratio (SNR) of the intrusion location for phase-sensitive optical time domain reflectometry (Ф-OTDR) system. By converting the spatial and time information of the Ф-OTDR traces into 2-D image, the proposed 2-D bilateral filtering algorithm can smooth the noise and preserve the useful signal efficiently. To simplify the algorithm, a Lorentz spatial function is adopted to replace the original Gaussian function, which has higher practicability. Furthermore, an adaptive parameter setting method is developed according to the relation between the optimal gray level standard deviation and noise standard deviation, which is much faster and more robust for different types of signals. In the experiment, the SNR of location information has been improved over 14 dB without spatial resolution loss for a signal with original SNR of 6.43 dB in 27.6 km sensing fiber. The proposed method has the potential to precisely extract intrusion location from a harsh environment with strong background noise.

Digital image processing and systems incorporating the same

Abstract: Algorithms for improving the quality of images tone mapped using the bilateral filter are presented. The algorithms involve a localized operator applied to the tone mapping compression factor which enhances apparent image contrast in the low dynamic range output of the tone mapping. At least one embodiment of the present invention is related to circuitry configured to perform at least a portion of related calculations.

An Efficient Adaptive Fuzzy Switching Weighted Mean Filter for Salt-and-Pepper Noise Removal

Abstract: An image degraded by noise is a common phenomenon. In this letter, we propose a novel adaptive fuzzy switching weighted mean filter to remove salt-and-pepper (SAP) noise. The process of denoising includes two stages: noise detection and noise elimination. In the first stage, pixels in a corrupted image are classified into two categories: original pixels and possible noise pixels. For the latter, we compute the maximum absolute luminance difference of processed pixels next to possible noise pixels to classify them into three categories: uncorrupted pixels, lightly corrupted pixels, and heavily corrupted pixels. In the second stage, under the assumption that pixels at a short distance tend to have similar values, the distance relevant weighted mean of the original pixels in the neighborhood of a noise pixel are computed. For a nonnoise pixel, retain it as unchanged; for a lightly corrupted pixel, replace it with the weighted average value of the weighted mean and its own value; and for a heavily corrupted pixel, change it to be the weighted mean. Experimental results show that compared to some state-of-the-art algorithms, our method keeps more texture details and is better at removing SAP noise and depressing artifacts.