کلمات کلیدی مربوط به کتاب پردازش تصویر برای دستگاه های جاسازی شده از داده های CFA تا کدگذاری تصویر-ویدئو: علوم و مهندسی کامپیوتر، پردازش داده های رسانه ای، پردازش تصویر
در صورت تبدیل فایل کتاب Image Processing for Embedded Devices. From CFA Data to Image-video Coding به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب پردازش تصویر برای دستگاه های جاسازی شده از داده های CFA تا کدگذاری تصویر-ویدئو نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Издательство Cambridge University Press, 2010, -307 pp.
Embedded imaging devices, such as
digital still and video cameras, mobile phones, personal
digital assistants, and visual sensors for surveillance and
automotive applications, make use of the single-sensor
technology approach. An electronic sensor (Charge Coupled
Device - CCD or Complementary Metal-Oxide-Semiconductor - CMOS)
is used to acquire the spatial variations in light intensity
and then uses image processing algorithms to reconstruct a
color picture from the data provided by the sensor. Acquisition
of color images requires the presence of different sensors for
different color channels. Manufacturers reduce the cost and
complexity by placing a color filter array (CFA) on top of a
single sensor, which is basically a monochromatic device, to
acquire color information of the true visual scene.
The overall performance of any device are the result of a
mixture of different components including hardware and software
capabilities and, not ultimately, overall design (i.e., shape,
weight, style, etc.).
This book is devoted to cover algorithms and methods for the
processing of digital images acquired by single-sensor imaging
devices. Typical imaging pipelines implemented in single-sensor
cameras are usually designed to find a trade-off between
sub-optimal solutions (devoted to solve imaging acquisition)
and technological problems (e.g., color balancing, thermal
noise, etc.) in a context of limited hardware resources. State
of the art techniques to process multichannel pictures,
obtained through color interpolation from CFA are very
advanced. On the other hand, not too much is known and
published about the application of image processing techniques
directly on CFA images, i.e. before the color interpolation
phase.
The various chapters of the book cover all aspects of
algorithms and methods for the processing of digital images
acquired by imaging consumer devices. More specifically, we
will introduce the fundamental basis of specific processing
into CFA domain (demosaicing, enhancement, denoising,
compression). Also ad-hoc matrixing and color balancing
techniques devoted to preprocess input data coming from the
sensor will be treated. In almost all cases various arguments
have been presented in a tutorial way in order to provide to
the readers a comprehensive overview of the main basis of each
involved topics. All contributors are well renowned experts in
the field as demonstrated by the number of related patents and
scientific publications.
The main part of the book analyzes the various aspects of the
imaging pipeline from the CFA data to image and video coding. A
typical imaging pipeline is composed by two functional modules
(pre-acquisition and post-acquisition) where the data coming
from the sensor in the CFA format are properly processed. The
term pre-acquisition is referred to the stage in which the
current input data coming from the sensor are analyzed just to
collect statistics useful to set parameters for correct
acquisition.
The book also contains a number of chapters that provide
solution and methods to address some undesired drawbacks of
acquired images (e.g., red-eye, jerkiness, etc.); an overview
of the current technologies to measure the quality of an image
is also given. Just considering the impressive (and fast)
growth in terms of innovation and available technology we
conclude the book just presenting some example of solution that
makes use of machine learning for image categorization and a
brief overview of recent trends and evolution in the field.
Fundamentals and HW/SW
Partitioning
Notions about Optics and Sensors
Exposure Correction
Pre-acquisition: Auto-focus
Color Rendition
Noise Reduction
Demosaicing and Aliasing Correction
Red Eyes Removal
Video Stabilization
Image Categorization
Image and Video Coding and Formatting
Quality Metrics
Beyond Embedded Devices