000			04729namaa22004211i 4500
003			OSt
005			20250223033333.0
008			241201s2023 |||a|||fr|m|| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a621.367
092			_a621.367 _221
097			_aM.Sc
099			_aCai01.13.08.M.Sc.2023.Ab.L
100	0		_aAbeer Ayman Ahmed Ali Elbehery, _epreparation.
245	1	0	_aLow Complexity Image Inpainting Using Autoencoder / _cby Abeer Ayman Ahmed Ali Elbehery ; Under the Supervision of Prof. Dr. Yasmine Aly Fahmy, Dr. Mai Badawi Kafafy.
246	1	5	_aترميم الصور بطريقة غير معقدة بإستخدام المشفر الآلي /
264		0	_c2023.
300			_a58 pages : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aThesis (M.Sc.)-Cairo University, 2023.
504			_aBibliography: pages 54-58.
520			_aImage inpainting is filling the missing or corrupted pixels in an image in a realistic way that cannot be differentiated by human eye. Traditionally, inpainting was done manually by artists to complete the missing regions in old paintings. In the digital processing era, image inpainting became an interesting research topic. The used techniques can be categorized into 2 categories; Non learning techniques and deep learning-based techniques. Non-learning techniques have been introduced since the year 2000. These methods include diffusion-based, patch-based and exemplar-based methods. Diffusion-based methods use partial differential equations to fill the image holes and ensure the continuity of edges along it. Patch-based methods search the whole image to find the perfect patches to complete the image, and Exemplar-based methods tend to merge both diffusion and patch-based methods. With the rise of deep learning, it is being widely used in image inpainting. The used models are capable of studying and learning the structure of the images to reconstruct the missing regions. Various models are introduced in literature for image inpainting including simple CNNs, autoencoders, GANs, DCGANs, and multi-stage networks. These models vary in the size, number of layers and number of parameters in the model. Non learning methods require simpler calculations, but they are only suitable for recovering images with simple structure and small missing regions. Deep learning- based methods have proven to be efficient for batch processing, and to fill holes of different sizes with better quality than non learning methods. But deep learning models require massive processing capabilities and long period of time for training, which may not be suitable in all cases. In this thesis, we access the complexity issue of training the image inpainting deep learning models. We propose an autoencoder architecture with some features added as skip connections, Adam optimizer and leaky ReLU, it has proven to outperforms other deep learning techniques in literature methods with lower processing and time complexity.
520			_aترميم الصور هي عملية إكمال الأجزاء الناقصة أو المدمرة من الصور بطريقة واقعية بحيث لا تستطيع العين التمييز بين الأجزاء الأصلية والأجزاء المرممة. التعلم العميق يستخدم بكثرة في ترميم الصور لأن له أداء أفضل من طرق الترميم التقليدية، ولكنه يحتاج إلى موارد معالجة ذات إمكانيات عالية ووقت أطول لتدريب النموذج المستخدم. النموذج المقترح يستخدم المشفر الآلي لترميم الصور، مع بعض التعديلات أثبت هذا النموذج أنه أفضل من بعض النماذج المستخدمة الأخرى من حيث إمكانيات المعالجة والوقت المستهلك.
530			_aIssued also as CD
546			_aText in English and abstract in Arabic & English.
650		7	_aImage processing ‪Digital techniques _2qrmak
653		0	_aImage inpainting _aAutoencoder _aSkip connections _aLow complexity _aAdam optimizer
700	0		_aYasmine Aly Fahmy _ethesis advisor.
700	0		_aMai Badawi Kafafy _ethesis advisor.
900			_b01-01-2023 _cYasmine Aly Fahmy _cMai Badawi Kafafy _dOmar Ahmed Nasr _dMohamed Farouk AbdElKader _UCairo University _FFaculty of Engineering _DDepartment of Electronics and Communications Engineering
905			_aEman _eHuda
942			_2ddc _cTH _e21 _n0
999			_c169130