Structurally and semantically coherent deep image inpainting

Structurally and semantically coherent deep image inpainting

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Bibliografiset tiedot
Päätekijät:	Sami, Mirza Tanzim, Khan, Ehsanul Amin, Rhidita, Ishrat Naiyer
Muut tekijät:	Uddin, Jia
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	BRAC University 2019
Aiheet:	Image inpainting DCGAN Deep learning Differential equations, Partial. Image restoration.
Linkit:	http://hdl.handle.net/10361/11445

Samankaltaisia teoksia

Introduction to partial differential equations /
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Julkaistu: (1995)

Ordinary and partial differential equations /
Tekijä: Raisighania, M. D.
Julkaistu: (2000)

Introduction to partial differential equations /
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Julkaistu: (1995)

Analytical solution of non-linear partial differential equation by using the extended (𝐆′/𝐆) expansion method with non-linear auxiliary equation
Tekijä: Karmaker, Bishwajit
Julkaistu: (2018)

Partial differential equations /
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Partial differential equations /
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Julkaistu: (1994)

Travelling wave and non-travelling wave solutions of nonlinear evolution equation via new generalized and Improved (G'/G) - expansion method
Tekijä: Khan, Anika Tahsin
Julkaistu: (2018)

Exact solutions of fractional differential equations by using new generalized (G´=G)-expansion method
Tekijä: Shafia, Humayra
Julkaistu: (2019)

Finite difference schemes and partial differential equations /
Tekijä: Strikwerda, John C., 1947-
Julkaistu: (2004)

Advances in nonlinear partial differential equations and stochastics /
Julkaistu: (1998)

Reconstruction of images with RPCA and augmenting 3D model
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Julkaistu: (2022)

Numerical solution of partial differential equations : finite difference methods /
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The (G'/G)-expansion method for abundant traveling wave solutions of Caudrey-Dodd-Gibbon equation
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Complex analytic methods for partial differential equations : an introductory text /
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Image processing and deep learning approach to evaluate earthquake resistance of urban buildings
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Numerical methods for nonlinear elliptic differential equations
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The exp-function method for new exact solutions of the nonlinear partial differential equations
Tekijä: Naher, Hasibun, et al.
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Boundary value problems for partial differential equations and applications in electrodynamics /
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Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
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Transfer learning based industrial steel plates fault diagnosis using industrial fault signals
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Early stage detection and classification of colon cancer using deep learning and explainable AI on histopathological images
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A comparative study of car image generation quality using DCGAN and VSGAN
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Generating and compressing images from a large volume of discrete datasets using GANs along with different compression techniques and studying the results
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Generalized and Improved (G′/G)-Expansion Method for (3+1)-Dimensional Modified KdV-Zakharov-Kuznetsev Equation
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New traveling wave solutions of the higher dimensional nonlinear evolution equation by the improved (G′/G) expansion method
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Extended generalized Riccati equation mapping method for the fifth-order Sawada-Kotera equation
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Multiscale methods

Image recognition by deep learning
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Generalized inverse and it’s applications to the solutions of system of linear equations and semigroup
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Deep learning based medical X-ray image recognition and classification
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An active-learning based training-schedule for biomedical image segmentation on deep neural networks
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A color vision approach for reconstructing color images in different lighting conditions based on auto encoder technique using deep neural networks
Tekijä: Gomes, Paul Richie, et al.
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Deep convolutional GAN-based data augmentation for medical image classification
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Julkaistu: (2022)

Elliptic differential equations : theory and numerical treatment /
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Julkaistu: (1992)

A first course in differential equations with modeling applications /
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Differential equations : an introduction to modern methods and applications /
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Detecting Deepfake images using deep convolutional neural network
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