Modified algorithms for image inpainting in Fourier transform domain

Tavakoli, Ali; Mousavi, Parisa; Zarmehi, Fatemeh

doi:10.1007/s40314-018-0632-4

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…e inpainting steps of the Criminisi image inpainting algorithm are composed of four parts: marking of the area to be repaired, the calculation of priority, searching and filling the best matching blocks, and confidence of updating. Aiming at the defects of the Criminisi algorithm, such as the long period of image inpainting, many researchers have improved the algorithm [27]. Image inpainting is implemented by the Filtersim simulation algorithm based on multipoint geostatistics [28].…”

Section: Image Inpainting Based On Combining the Multipointmentioning

confidence: 99%

Study on Extraction Methods for Different Components in a Carbonate Digital Core

Xiao

Zhang

et al. 2020

Mathematical Problems in Engineering

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It is difficult to carry out petrophysical experiments because of the serious damage caused to cores in the development of fractures and pores in carbonate reservoirs. The development of a three-dimensional digital core in carbonate reservoirs has become a hot topic in rock physics research. Compared with the three-dimensional digital core, including basic rock skeletons and pores in sandstone reservoirs, carbonate reservoirs also include secondary structures such as microfractures. The carbonate contains different components, and extracting these components is a very difficult problem. The resolution on the electrical image log image is high, which can clearly reflect the macrocomponents in various reservoirs. There are some blank areas between electrodes on the electrical image log, which affects the extraction of components in a three-dimensional digital core. Aiming at the serious heterogeneities in the carbonate reservoirs and affecting image inpainting on the electrical image log image, a new method of image inpainting based on a combination of multipoint geostatistics and an interpolation method is put forward. The experimental results show that this method generates faster and better full-bore images than other methods. Due to the multipeak histogram, the maximum interclass variance in the two times method is proposed to extract macrocomponents such as basic rock skeletons, pores, and connected parts. The microfractures can be extracted from the CT scanned images by using image segmentation from the combination of the watershed and OTSU methods. The experimental results prove that using extraction methods for different components enables better results to be obtained.

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Section: Image Inpainting Based On Combining the Multipointmentioning

confidence: 99%

Study on Extraction Methods for Different Components in a Carbonate Digital Core

Xiao

Zhang

et al. 2020

Mathematical Problems in Engineering

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“…The image is transformed from the spatial domain to the frequency domain for image enhancement processing [27]. Then the image is returned from the frequency domain to the spatial domain, and the enhanced image can be obtained [28].…”

Section: Improved Butterworth Filtermentioning

confidence: 99%

Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Hao

Wang²,

2019

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At present, the identification of haze levels mostly relies on traditional measurement methods, the real-time operation and convenience of these methods are poor. This paper aims to realize the identification of haze levels based on the method of haze images processing. Therefore, this paper divides the haze images into five levels, and obtains the high-quality haze images in each level by the brightness correction of the optimization solution and the color correction of the feature matching. At the same time, in order to reduce the noise of the haze images, this article improved the Butterworth filter. Finally, based on the processed haze images, this paper uses the Faster R-CNN network to identify the haze levels. The results of multiple sets of comparison experiments demonstrate the accuracy of the study.

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“…The DCT was introduced to shorten the training time and enhance the accuracy of the CNN in classification of rock images. The DCT, the real part of Fourier transform, approximates an image with a set of cosine functions (basis functions), which differ in frequency and amplitude [17]. Therefore, the DCT can be regarded as a simplified Fourier transform.…”

Section: Dctmentioning

confidence: 99%

Automatic Recognition of Rock Images Based on Convolutional Neural Network and Discrete Cosine Transform

Li¹,

Shi²,

Bu³

2019

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This paper aims to overcome two major defects with the traditional rock image classification framework based on convolutional neural network (CNN), namely, slow training and poor classification accuracy. First, the causes of the two defects were analyzed in details. Through the analysis, the slow training is attributable to the information redundancy in the original image, and the classification error to the lack of differentiation of rock features extracted from the spatial domain. Therefore, the original image was divided into multiple blocks of equal size, and the discrete cosine transform (DCT) was introduced to process each block. After the transform, ten or fifteen frequency coefficients in the upper left corner of the 2D frequency coefficient matrix were retained, and added to the traditional CNN framework for image classification. Experimental results show that the proposed DCT-CNN framework outperformed the CNN framework in training time and classification accuracy.

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Modified algorithms for image inpainting in Fourier transform domain

Cited by 8 publications

References 19 publications

Study on Extraction Methods for Different Components in a Carbonate Digital Core

Study on Extraction Methods for Different Components in a Carbonate Digital Core

Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Automatic Recognition of Rock Images Based on Convolutional Neural Network and Discrete Cosine Transform

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