An adaptive bilateral filtering method based on improved convolution kernel used for infrared image enhancement

Lv, Hui; Shan, Pengfei; Shi, Hongfang; Zhao, Li

doi:10.1007/s11760-022-02188-1

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…Wavelet decomposition of an image uses a series of filter sets to filter the image to obtain sub-bands of different frequencies. The Mallet decomposition algorithm is commonly used in the wavelet image decomposition algorithm, and its expression is: vertical, and diagonal orientations, as respectively represented with LH, HL, and HH [14,15] . The schematic diagram of image decomposition is illustrated in Figure 2.…”

Section: Wavelet Image Decompositionmentioning

confidence: 99%

Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering

Yang

Tong

Cao

et al. 2023

J. Phys.: Conf. Ser.

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Aiming at the problems such as the poor visual effect of infrared thermal imaging under low illumination conditions, an enhanced infrared image recognition method based on wavelet decomposition is proposed for this paper. First, according to wavelet decomposition, the infrared image is divided into a low-frequency sub-band (LFS) and high-frequency sub-band (HFS) in each orientation, and HFS in each orientation is denoised using a modified wavelet threshold function to strengthen the denoising effect of the infrared image; Second, the multi-scale Retinex (MSR) algorithm based on weighted guided filtering (WGIF) is used to evaluate the component of illumination in the basic layer of the LFS, and the WGIF-segmented detail layer images are fused with the MSR processed ones to effectively highlight the texture details of the LFS; Finally, the LFS and the HFS are wavelets reconstructed (WR) to obtain the infrared enhanced images. This paper applies the algorithm to a low-resolution (resolution 32x32 pixels) infrared thermal imaging module, and the results of combining subjective and objective evaluation indexes show that the overall property index of the algorithm in this paper is superior to other contrasting algorithms and can effectively improve the infrared image quality.

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Section: Wavelet Image Decompositionmentioning

confidence: 99%

Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering

Yang

Tong

Cao

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…Wang, N., Hou, T., Zhang, T., Archives of Transport, 68(4), [7][8][9][10][11][12][13][14][15][16][17][18][19][20]2023 Final Estimation Stage: the conventional BM3D algorithm employs a Wiener filter optimized based on mean square error (MSE). MSE is an image quality evaluation metric that relies on pixel-wise errors and overlooks local information in non-smooth regions.…”

Section: Image Filteringmentioning

confidence: 99%

“…To obtain highquality railway track images, this study combines bilateral filtering with the improved BM3D algorithm for image filtering. Bilateral filtering (Lv et al, 2022) is a nonlinear spatial filtering technique that can effectively preserve edges while achieving noise reduction and smoothing. The output pixel value G of the noisy image after bilateral filtering is expressed as follows: The spatial domain kernel d is based on a Gaussian function to calculate the Euclidean distance between the current point and the centroid, defined as follows:…”

Section: Image Filteringmentioning

confidence: 99%

“…Wang, N., Hou, T., Zhang, T., Archives of Transport, 68(4),[7][8][9][10][11][12][13][14][15][16][17][18][19][20] 2023 lower detection clarity in scenario B compared to the classical Canny algorithm. On the other hand, the proposed algorithm in this paper detects railway edge images with higher IED values and improves the edge detection time.…”

mentioning

confidence: 99%

“…Archives of Transport, 68(4),[7][8][9][10][11][12][13][14][15][16][17][18][19][20] 2023 enhance the robustness of the algorithm when applied in edge detection. It provides a simple calculation and is less affected by variations in image brightness.…”

mentioning

confidence: 99%

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Research on railway track edge detection based on BM3D and Zernike moments

Wang,

Hou,

Zhang

2023

AoT

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With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.

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Multi-filter-Based Image Pre-processing on Face Mask Detection Using Custom CNN Architecture

Kayali,

Dimililer

2024

Smart Innovation, Systems and Technologies

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An adaptive bilateral filtering method based on improved convolution kernel used for infrared image enhancement

Cited by 9 publications

References 20 publications

Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering

Infrared image enhancement algorithm based on improved wavelet threshold function and weighted guided filtering

Research on railway track edge detection based on BM3D and Zernike moments

Multi-filter-Based Image Pre-processing on Face Mask Detection Using Custom CNN Architecture

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