Image enhancement by stochastic homomorphic filtering

Fries, Robert W.; Modestino, J.W.

doi:10.1109/icassp.1979.1170794

Cited by 7 publications

(6 citation statements)

References 9 publications

(2 reference statements)

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“…The multi-layered preprocessing structure includes a combination of filtering and feature extraction techniques applied in sequence to fulfill different functions. The first layer, one of the key components in the MLP, is homomorphic filtering [35], used to process concrete surface images in the frequency domain. For image processing, homomorphic filtering is able to suppress low frequency components, such as those associated with lighting variations, while highlighting high-frequency components associated with local details such as crack edges.…”

Section: Mlp-cnn Frameworkmentioning

confidence: 99%

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

Wang

et al. 2020

Sensors

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Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

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Section: Mlp-cnn Frameworkmentioning

confidence: 99%

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

Wang

et al. 2020

Sensors

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“…The idea of image preprocessing algorithms is adopting various types of image enhancement methods before face recognition. There are four kinds of commonly used enhancement methods [5,6]: grey level transformation with histogram equalisation as the representative [7][8][9][10][11][12], homomorphic filtering base on illumination reflectance model [13][14][15][16][17][18], light compensation based on Retinex theory [19][20][21][22][23][24], as well as image enhancement in gradient domain [25][26][27][28][29][30]. Grey level transformation based on global histogram equalisation can enhance image's overall contrast and is suitable for enhancing images which have lower overall grey value or dynamic range.…”

Section: Introductionmentioning

confidence: 99%

A novel face recognition method based on fusion of LBP and HOG

Chen

Gao

et al. 2021

IET Image Processing

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As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by center symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Tarik Arici [4] presented a general framework based on histogram equalization for image contrast enhancement. The latter is mainly based on the Fourier transform of the image to improve the image spectrum by enhancing or inhibiting part of the spectrum, such as low-pass filtering technology, highpass filtering technology and homomorphic filtering [5].The typical transform domain method is frequency filtering [6]. Muhammad Zafar Iqbal [6] combined dual-tree complex wavelet transform with nonlocal means for resolution enhancement of satellite images.…”

Section: Introductionmentioning

confidence: 99%

Dictionary learning based image enhancement for rarity detection

Liu

Wang

2014

2014 12th International Conference on Signal Processing (ICSP)

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Image enhancement is an important image processing technique that processes images suitably for a specific application e.g. image editing. The conventional solutions of image enhancement are grouped into two categories which are spatial domain processing method and transform domain processing method such as contrast manipulation, histogram equalization, homomorphic filtering. This paper proposes a new image enhance method based on dictionary learning. Particularly, the proposed method adjusts the image by manipulating the rarity of dictionary atoms. Firstly, learn the dictionary through sparse coding algorithms on divided subimage blocks. Secondly, compute the rarity of dictionary atoms on statistics of the corresponding sparse coefficients. Thirdly, adjust the rarity according to specific application and form a new dictionary. Finally, reconstruct the image using the updated dictionary and sparse coefficients. Compared with the traditional techniques, the proposed method enhances image based on the image content not on distribution of pixel grey value or frequency. The advantages of the proposed method lie in that it is in better correspondence with the response of the human visual system and more suitable for salient objects extraction. The experimental results demonstrate the effectiveness of the proposed image enhance method.Index Terms-image enhancement, sparse coding, K-SVD algorithm, dictionary learning, rarity

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Image enhancement by stochastic homomorphic filtering

Cited by 7 publications

References 9 publications

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

A novel face recognition method based on fusion of LBP and HOG

Dictionary learning based image enhancement for rarity detection

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