Fundamentals of Digital Image Processing

Vyas, Aparna; Yu, Shuhui; Paik, Joonki

doi:10.1007/978-981-10-7272-7_1

Cited by 31 publications

(24 citation statements)

References 4 publications

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“…Flipping (Vyas et al 2018 ) reflects an image around its vertical axis or horizontal axis or both vertical and horizontal axis. It helps users to maximize the number of images in a dataset without needing any artificial processing..…”

Section: Classical Image Data Augmentationmentioning

confidence: 99%

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A comprehensive survey of recent trends in deep learning for digital images augmentation

2021

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Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.

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Section: Classical Image Data Augmentationmentioning

confidence: 99%

“…Shearing (Vyas et al 2018 ) is the change of the original image along the direction as well as the direction. It is an ideal technique to change the shape of an existing object in an image.…”

Section: Classical Image Data Augmentationmentioning

confidence: 99%

A comprehensive survey of recent trends in deep learning for digital images augmentation

2021

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“…Sebuah ciri biologi yang dapat memberikan informasi untuk mengidentifikasi masing-masing individu. Dalam teknologi informasi, biometrik biasanya merujuk kepada teknologi yang mengukur dan menganalisis karakteristik tubuh manusia seperti sidik jari, retina, mata, pola suara, dan pola wajah yang terutama digunakan untuk proses otentifikasi [7] [8]. Jaringan saraf tiruan mampu mengenali sesuatu dengan berbasis masa lalu.…”

Section: Pendahuluanunclassified

“…PCA yaitu suatu metode yang mampu mereduksi dimensi dari suatu objek, sehingga ukuran dari objek yang akan lebih ringkas dan mampu mengambil karakteristik yang penting dari suatu objek yang akan diolah. Jika dimensi dari objek lebih kecil dan informasi yang lebih padat, maka objek tersebut akan lebih spesifik dibanding objek yang diolah sebelumnya [8] [13]. Pengenalan pola wajah merupakan suatu pengenalan pola (pattern recognition) yang khusus untuk kasus wajah [14].…”

Section: Pendahuluanunclassified

Pengenalan Ekspresi Raut Wajah Berbasis Jaringan Saraf Tiruan Backpropagation Dengan Metode Principal Component Analysis

Harizahayu

2021

BAREKENG: J. Il. Mat. & Ter.

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The development of artificial neural networks is related to statistical and biometric analysis which is one of the applications that can require artificial neural network models. Recognition of facial patterns is an important part of identifying a person. The face can be divided into areas such as the nose, eyes and mouth. Face pattern recognition is a research area that can be applied to the principal component analysis (PCA) method. The training process carried out by the eigenface calculation uses PCA and the results of this study show that facial pattern recognition based on the proportion of memorization and generalization for the use of the method without PCA is better than facial pattern recognition using PCA. Pattern recognition without using the PCA method, the level of memorization and generalization reaches 100% at the 40th iteration and 0.0099 error with a learning rate and momentum of 0.8, while facial pattern recognition using the PCA method, the memorization and generalization level reaches 100% in the iteration. to -1000 and error 0.00103 with learning rate and momentum 0.9.

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“…An image is a discrete representation of data that has layout information and the color intensity of an object [1]. Nowadays, an image can be easily converted into digital content, but sometimes there is additional noise in the image during camera capture so that the image appears damaged.…”

Section: Introductionmentioning

confidence: 99%

Joint method using Akamatsu and discrete wavelet transform for image restoration

Akbar

Setiadi

2020

ACI

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Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

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Fundamentals of Digital Image Processing

Cited by 31 publications

References 4 publications

A comprehensive survey of recent trends in deep learning for digital images augmentation

A comprehensive survey of recent trends in deep learning for digital images augmentation

Pengenalan Ekspresi Raut Wajah Berbasis Jaringan Saraf Tiruan Backpropagation Dengan Metode Principal Component Analysis

Joint method using Akamatsu and discrete wavelet transform for image restoration

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