Hausdorff Derivative Laplacian Operator for Image Sharpening

Wang, Fajie; Chen, Wen; Qiu, Lin

doi:10.1142/s0218348x19500609

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…In the Fourier spectrogram, background noise is often present as low-frequency components, while foreground features are present as high-frequency ones. In image processing, high-pass filtering is widely used in image enhancement, where high-frequency signals pass through the filter and lowfrequency components are suppressed [27][28][29]. So, a highpass filter has the effect of highlighting foreground features and enhancing the edges of the targets, thus pronouncing the differences between foreground and background features.…”

Section: Feature Enhancement and Feedback Propagationmentioning

confidence: 99%

Object detection algorithm based on feature enhancement

Zheng¹,

Wang²,

Wang³

2021

Meas. Sci. Technol.

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Recently, many excellent algorithms have made great progress in object detection, but there are also problems in these algorithms’ performance on targets of different sizes, and in particular in small object detection. Aiming at the problem of insufficient feature representation by the feature extractor, in this paper we propose a lightweight algorithm to improve feature extraction. The algorithm includes three modules. First, considering that the shallow features in feature extraction contain much background noise, in this paper we design a multi-level feedback propagation model based on a Gaussian high-pass filter. The shallow layers are enhanced using the filter and then back-propagated to add the upper shallow layer features and obtain new shallow layer features. This process is performed on the newly generated shallow layer for n iterations, which is beneficial for enhancing targets in the foreground area and suppressing background noise. Second, we form a stacked dilated convolution module with different dilation rates to cover the entire deep feature layer densely, which enlarges the receptive field and enriches the contextual information. Finally, we build a multi-scale fusion module to fuse the above-mentioned enhanced shallow and deep features to obtain output features with powerful representational ability for detection tasks. In addition, the model is easily embedded into existing approaches to enhance their performance. We build the model on the VGG-16 and ResNet-50 backbones and successfully applied it on Darknet-19 and Darknet-53 to verify its effectiveness and stability. The experiments on the COCO dataset prove that the proposed algorithm outperforms the state-of-art methods, with a mean average precision improvement reaching 2% on average. The effect is remarkable on small targets and complex backgrounds. Furthermore, it does not affect the detection speed significantly, so real time detection requirements can still be met.

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Section: Feature Enhancement and Feedback Propagationmentioning

confidence: 99%

Object detection algorithm based on feature enhancement

Zheng¹,

Wang²,

Wang³

2021

Meas. Sci. Technol.

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“…Another innovative work to extend Hausdorff derivatives to image sharpening. The Hausdorff derivative Laplace equation (HDLO) is computed through Hausdorff fractal distance [32]. A hybrid deblurring strategy introduced by Chang et al tends to process strong edges and weak edges separately.…”

Section: • Approximation Of Edges Is More Accuratementioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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“…LBP 本质上是图像的一阶导数模式, 是一种由二值导数方向组合而成的微模式. 但是一阶导数模式提取的特征图往往会产生较粗的边缘, 轮廓不够清晰, 对图像细节的表征能力不够 [17][18] . 高阶导数对图像精细细节(如细线、边缘等灰度变化剧烈区域)的增强作用要比一阶导数好得多, 但是其对孤立像素点的响应也更为强烈, 容易放大噪声, 这也是其弊端.…”

Section: 人脸识别大多数研究中都使用了纹理分析和分类纹理反映了图像的视觉特征表征一个物体unclassified

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

Ye¹,

Wang²,

Ying³

et al. 2021

J Comput-Aided Design Comput Graphics

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Pointing to weak robustness caused by the noise sensitivity and feature redundancy of present face recognition methods with high-order features, a new method of the local high-order principal direction pattern based on "gradient face" is proposed. Firstly, the gradient face convolution operator designed is used to compute the sum of multi-directional gradient components of pixels to construct a gradient face. Then, the principal direction grouping strategy is introduced on the gradient face to characterize its high-order derivative features, and a principal direction feature map is formed according to the feature code of high-order derivatives direction changes in local neighborhood. Finally, block statistics and cascading of histogram features are made a vector to be input in to a support vector machine for multi-classification. Experimental results of several public face databases show that the proposed method is robust to changes in illumination, expression, and facial occlusion and has higher recognition efficiency.

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Hausdorff Derivative Laplacian Operator for Image Sharpening

Cited by 11 publications

References 20 publications

Object detection algorithm based on feature enhancement

Object detection algorithm based on feature enhancement

Mini Kirsch Edge Detection and Its Sharpening Effect

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

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Hausdorff Derivative Laplacian Operator for Image Sharpening

Cited by 11 publications

References 20 publications

Object detection algorithm based on feature enhancement

Object detection algorithm based on feature enhancement

Mini Kirsch Edge Detection and Its Sharpening Effect

Face Recognition with Local High-Order Principal Direction Pattern Based on &ldquo;Gradient Face&rdquo;

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Product

Resources

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Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”