Low Complexity Image Inpainting Using Autoencoder / (Record no. 169130)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04729namaa22004211i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20250223033333.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241201s2023 |||a|||fr|m|| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621.367 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 621.367 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.13.08.M.Sc.2023.Ab.L |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Abeer Ayman Ahmed Ali Elbehery, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Low Complexity Image Inpainting Using Autoencoder / |
| Statement of responsibility, etc. | by Abeer Ayman Ahmed Ali Elbehery ; Under the Supervision of Prof. Dr. Yasmine Aly Fahmy, Dr. Mai Badawi Kafafy. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | ترميم الصور بطريقة غير معقدة بإستخدام المشفر الآلي / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 58 pages : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.)-Cairo University, 2023. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 54-58. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Image inpainting is filling the missing or corrupted pixels in an image in a realistic<br/>way that cannot be differentiated by human eye. Traditionally, inpainting was done<br/>manually by artists to complete the missing regions in old paintings. In the digital<br/>processing era, image inpainting became an interesting research topic. The used<br/>techniques can be categorized into 2 categories; Non learning techniques and deep<br/>learning-based techniques.<br/>Non-learning techniques have been introduced since the year 2000. These methods<br/>include diffusion-based, patch-based and exemplar-based methods. Diffusion-based<br/>methods use partial differential equations to fill the image holes and ensure the<br/>continuity of edges along it. Patch-based methods search the whole image to find the<br/>perfect patches to complete the image, and Exemplar-based methods tend to merge both<br/>diffusion and patch-based methods.<br/>With the rise of deep learning, it is being widely used in image inpainting. The<br/>used models are capable of studying and learning the structure of the images to<br/>reconstruct the missing regions. Various models are introduced in literature for image<br/>inpainting including simple CNNs, autoencoders, GANs, DCGANs, and multi-stage<br/>networks. These models vary in the size, number of layers and number of parameters in<br/>the model.<br/>Non learning methods require simpler calculations, but they are only suitable for<br/>recovering images with simple structure and small missing regions. Deep learning-<br/>based methods have proven to be efficient for batch processing, and to fill holes of<br/>different sizes with better quality than non learning methods. But deep learning models<br/>require massive processing capabilities and long period of time for training, which may<br/>not be suitable in all cases. In this thesis, we access the complexity issue of training the<br/>image inpainting deep learning models. We propose an autoencoder architecture with<br/>some features added as skip connections, Adam optimizer and leaky ReLU, it has<br/>proven to outperforms other deep learning techniques in literature methods with lower<br/>processing and time complexity. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | ترميم 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 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issued also as CD |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Image processing Digital techniques |
| Source of heading or term | qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Image inpainting |
| -- | Autoencoder |
| -- | Skip connections |
| -- | Low complexity |
| -- | Adam optimizer |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Yasmine Aly Fahmy |
| Relator term | thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mai Badawi Kafafy |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2023 |
| Supervisory body | Yasmine Aly Fahmy |
| -- | Mai Badawi Kafafy |
| Discussion body | Omar Ahmed Nasr |
| -- | Mohamed Farouk AbdElKader |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Electronics and Communications Engineering |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Eman Ghareeb |
| Reviser Names | Huda |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 01.12.2024 | 89115 | Cai01.13.08.M.Sc.2023.Ab.L | 01010110089115000 | 01.12.2024 | 01.12.2024 | Thesis |