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020 _a9783031015298
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024 7 _a10.1007/978-3-031-01529-8
_2doi
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082 0 4 _a621,382
_223
100 1 _aAndrade, Juan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979541
245 1 2 _aA Survey of Blur Detection and Sharpness Assessment Methods
_h[electronic resource] /
_cby Juan Andrade.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXVII, 95 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aSignal processing.
_94052
650 1 4 _aSignal, Speech and Image Processing.
_931566
710 2 _aSpringerLink (Online service)
_979542
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000171
776 0 8 _iPrinted edition:
_z9783031004018
776 0 8 _iPrinted edition:
_z9783031026577
830 0 _aSynthesis Lectures on Algorithms and Software in Engineering,
_x1938-1735
_979543
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01529-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84797
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