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Image Super-Resolution: A Complete Guide: Single Image Super-Resolution and Its Applications - Softcover
Synopsis
Image/video super-resolution are research thrust areas in recent times. Their applications include HDTV, image coding, image resizing, image manipulation, remote sensing, face recognition, astronomy, and surveillance. The objective is to increase image/ video resolution through upsampling, deblurring, denoising, deep learning etc. The development of various image/ video super-resolution theories has been studied in this book focusing on Deep convolutional networks–based super-resolution (DeepCNSR). More than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution have been exhaustively analyzed with its merits and demerits. A taxonomy with nine categories for DeepCNSR networks has been introduced including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. Network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters) of each model have been studied comparatively.
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Image Super-Resolution: A Complete Guide
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LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164667
ISBN 13: 9786206164661
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Image/video super-resolution are research thrust areas in recent times. Their applications include HDTV, image coding, image resizing, image manipulation, remote sensing, face recognition, astronomy, and surveillance. The objective is to increase image/ video resolution through upsampling, deblurring, denoising, deep learning etc. The development of various image/ video super-resolution theories has been studied in this book focusing on Deep convolutional networks-based super-resolution (DeepCNSR). More than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution have been exhaustively analyzed with its merits and demerits. A taxonomy with nine categories for DeepCNSR networks has been introduced including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. Network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters) of each model have been studied comparatively. 152 pp. Englisch. Seller Inventory # 9786206164661
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Image Super-Resolution: A Complete Guide
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ISBN 13: 9786206164661
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Image/video super-resolution are research thrust areas in recent times. Their applications include HDTV, image coding, image resizing, image manipulation, remote sensing, face recognition, astronomy, and surveillance. The objective is to increase image/ vid. Seller Inventory # 894903801
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Image Super-Resolution: A Complete Guide | Single Image Super-Resolution and Its Applications
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ISBN 10: 6206164667
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Taschenbuch. Condition: Neu. Image Super-Resolution: A Complete Guide | Single Image Super-Resolution and Its Applications | Sangita Roy | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206164661 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 127179737
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Image Super-Resolution: A Complete Guide
Published by
LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164667
ISBN 13: 9786206164661
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Image/video super-resolution are research thrust areas in recent times. Their applications include HDTV, image coding, image resizing, image manipulation, remote sensing, face recognition, astronomy, and surveillance. The objective is to increase image/ video resolution through upsampling, deblurring, denoising, deep learning etc. The development of various image/ video super-resolution theories has been studied in this book focusing on Deep convolutional networks-based super-resolution (DeepCNSR). More than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution have been exhaustively analyzed with its merits and demerits. A taxonomy with nine categories for DeepCNSR networks has been introduced including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. Network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters) of each model have been studied comparatively.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch. Seller Inventory # 9786206164661
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Image Super-Resolution: A Complete Guide : Single Image Super-Resolution and Its Applications
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LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206164667
ISBN 13: 9786206164661
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image/video super-resolution are research thrust areas in recent times. Their applications include HDTV, image coding, image resizing, image manipulation, remote sensing, face recognition, astronomy, and surveillance. The objective is to increase image/ video resolution through upsampling, deblurring, denoising, deep learning etc. The development of various image/ video super-resolution theories has been studied in this book focusing on Deep convolutional networks-based super-resolution (DeepCNSR). More than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution have been exhaustively analyzed with its merits and demerits. A taxonomy with nine categories for DeepCNSR networks has been introduced including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. Network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters) of each model have been studied comparatively. Seller Inventory # 9786206164661