Computational Intelligence Methods for Super-Resolution in Image Processing Applications (Hardcover, 1st ed. 2021)


This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.

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Product Description

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.

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Product Details

General

Imprint

Springer Nature Switzerland AG

Country of origin

Switzerland

Release date

May 2021

Availability

Expected to ship within 12 - 17 working days

First published

2021

Editors

, ,

Dimensions

235 x 155mm (L x W)

Format

Hardcover

Pages

305

Edition

1st ed. 2021

ISBN-13

978-3-03-067920-0

Barcode

9783030679200

Categories

LSN

3-03-067920-9



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