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008			230204s2021 sz | s |||| 0|eng d
020			_a9783031015298
024	7		_a10.1007/978-3-031-01529-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637 _b2021 EB
100	1		_aAndrade, Juan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686567
245	1	2	_aA Survey of Blur Detection and Sharpness Assessment Methods _cby Juan Andrade
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVII, 95 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Algorithms and Software in Engineering _x1938-1735
505	0		_aPreface -- Acknowledgments -- Introduction -- Out-of-Focus Blur -- Image Quality Assessment -- No-Reference Image Assessment -- Summary and Future Directions -- Bibliography -- Author's Biography .
520			_aBlurring is almost an omnipresent effect on natural images. The main causes of blurring in images include: (a) the existence of objects at different depths within the scene which is known as defocus blur; (b) blurring due to motion either of objects in the scene or the imaging device; and (c) blurring due to atmospheric turbulence. Automatic estimation of spatially varying sharpness/blurriness has several applications including depth estimation, image quality assessment, information retrieval, image restoration, among others. There are some cases in which blur is intentionally introduced or enhanced; for example, in artistic photography and cinematography in which blur is intentionally introduced to emphasize a certain image region. Bokeh is a technique that introduces defocus blur with aesthetic purposes. Additionally, in trending applications like augmented and virtual reality usually, blur is introduced in order to provide/enhance depth perception. Digital images and videos are produced every day in astonishing amounts and the demand for higher quality is constantly rising which creates a need for advanced image quality assessment. Additionally, image quality assessment is important for the performance of image processing algorithms. It has been determined that image noise and artifacts can affect the performance of algorithms such as face detection and recognition, image saliency detection, and video target tracking. Therefore, image quality assessment (IQA) has been a topic of intense research in the fields of image processing and computer vision. Since humans are the end consumers of multimedia signals, subjective quality metrics provide the most reliable results; however, their cost in addition to time requirements makes them unfeasible for practical applications. Thus, objective quality metrics are usually preferred.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9413188 _aProceso digital de imágenes
650		7	_2embne _9156182 _aVídeo digital
776	0	8	_iPrinted edition: _z9783031000171
776	0	8	_iPrinted edition: _z9783031004018
776	0	8	_iPrinted edition: _z9783031026577
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01529-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI