Fast Guided Median Filter

Mishiba, Kazu

doi:10.1109/tip.2022.3232916

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…The 1-pass 2D filter [58] is highly cache-efficient with fewer synchronizations, although only multichannel data can be vectorized. This is useful for the multichannel-friendly filter [32], [59] and constant-time stereo matching [60]. An efficient vectorization method [21] using FMA has also been proposed for pure SDCT filters.…”

Section: B Short-time Fourier and Sliding Transformsmentioning

confidence: 99%

SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide

Kanetaka,

Takagi,

Maeda

et al. 2024

IEEE Access

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Section: B Short-time Fourier and Sliding Transformsmentioning

confidence: 99%

SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide

Kanetaka,

Takagi,

Maeda

et al. 2024

IEEE Access

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“…In order to eliminate the image noise, the weighted average method is used to gray the image. The median filtering method [9] with a filter kernel of 5×5 is used to remove the noise in the road image and retain its edge texture information. After grayscale and filtering processing, in order to contrast between the road area and the background, the power-law transformation [10] is used to convert the road grayscale into a concentrated highbrightness area into a low-brightness area.…”

Section: Roi Image Preprocessingmentioning

confidence: 99%

Segmentation method of drivable areas of mine roads based on rut texture features

Yuan,

Huang,

Xiong

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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The mine road is unstructured, the line shape is changeable, and the color of the road surface is similar to that of the mountains on both sides. In order to make up for the deficiency of the segmentation effect of the road detection model based on shape and color, a driving area segmentation method based on rut texture features is proposed. Initially, the collected images are preprocessed in the region of interest. Secondly, the gray level co-occurrence matrix(GLCM) is used to obtain the characteristic parameters of the rutting area, and four texture feature indexes are used as the input feature vectors of the genetic algorithm(GA) to obtain the optimal segmentation threshold. Finally, by filling the hole noise in the segmented image, a complete drivable area is obtained. The comparison test shows that the proposed segmentation method based on rut texture features can effectively overcome the problem that the road is similar to the background color and is difficult to segment. Compared with other similar segmentation methods, the segmentation effect of the road has better accuracy and robustness.

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“…Using edge-preserving filtering, ( 13 ) of the final step is replaced as:

where

represents any joint edge-preserving filtering with the guidance signal set as

. Examples of the final step filtering are high-dimensional filtering (high-dimensional Gaussian filtering [ 47 , 48 ], guided image filtering [ 20 , 71 ], domain transform filtering [ 33 ], adaptive manifold filtering [ 30 ]), frequency transform filtering ( edge-avoiding wavelet [ 72 , 73 ], redundant frequency transform [ 74 ]), adaptive filtering (range parameter adaptive filtering [ 75 ]), enhancement filtering (local Laplacian filtering [ 76 , 77 , 78 ]), statical filtering (fast guided median filtering [ 79 ]), LUT-based filtering [ 80 ], optimization-based filtering (weighted least square optimization [ 81 , 82 ], and L0 smoothing optimization [ 83 ]).…”

Section: Extension Of Decomposed Multilateral Filteringmentioning

confidence: 99%

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

Nogami,

Kanetaka,

Naganawa

et al. 2024

Sensors

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This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications.

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Fast Guided Median Filter

Cited by 10 publications

References 39 publications

SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide

SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide

Segmentation method of drivable areas of mine roads based on rut texture features

Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images

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