Morlet Wavelet transformed holograms for numerical adaptive view-based reconstruction

Viswanathan, Kartik; Gioia, Patrick; Morin, Luce

doi:10.1117/12.2061588

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…In some cases, the coding techniques that have been used for holograms compression are the current standards for image and video compression [38], [39]. In some researches, new elements have been added to the compression process in order to improve performance [39]- [41], and also wavelet transforms have been proposed, different from the standard ones, such as the Gabor wavelet [42] and the Morlet wavelet [43].…”

Section: Digital Holography Data Compressionmentioning

confidence: 99%

Digital holography data compression

Corda¹

2019

Telfor J

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Digital holography processing is a research topic related to the development of novel visual immersive applications. The huge amount of information conveyed by a digital hologram and the different properties of holographic data with respect to conventional photographic data require a comprehension of the performances and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of the state-of-the-art compression techniques applied to digital holographic data.

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Section: Digital Holography Data Compressionmentioning

confidence: 99%

Digital holography data compression

Corda¹

2019

Telfor J

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“…In 2013, Blinder et al [39] investigated alternative wavelet decomposition on off-axis holograms. In 2014, Viswanathan et al [40] used Morlet wavelets for transforming a hologram, and in 2015, Xing et al [41] combined wavelet transform and joint encoding methods to compress phase-shifting holographic data.…”

Section: Existing Literature On Holography Codingmentioning

confidence: 99%

Holographic representation: Hologram plane vs. object plane

Bernardo

Fernandes

Arrifano

et al. 2018

Signal Processing: Image Communication

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Digital holography allows the recording, storage and subsequent reconstruction of both amplitude and phase of the light field scattered by an object. This is accomplished by recording interference patterns that preserve the properties of the original object field essential for 3D visualization, the so-called holograms. Digital holography refers to the acquisition of holograms with a digital sensor, typically a CCD or a CMOS camera, and to the reconstruction of the 3D object field using numerical methods. In the current work, the different representations of digital holographic information in the hologram and in the object planes are studied. The coding performance of the different complex field representations, notably Amplitude-Phase and Real-Imaginary, in both the hologram plane and the object plane, is assessed using both computer generated and experimental holograms. The HEVC intra main coding profile is used for the compression of the different representations in both planes, either for experimental holograms or computer generated holograms. The HEVC intra compression in the object plane outperforms encoding in the hologram plane. Furthermore, encoding computer generated holograms in the object plane has a larger benefit than the same encoding over the experimental holograms. This difference was expected, since experimental holograms are affected by a larger negative influence of speckle noise, resulting in a loss of compression efficiency. This work emphasizes the possibility of holographic coding on the object plane, instead of the common encoding in the hologram plane approach. Moreover, this possibility allows direct visualization of the Object Plane Amplitude in a regular 2D display without any transformation methods. The complementary phase information can easily be used to render 3D features such as depth map, multi-view or even holographic interference patterns for further 3D visualization depending on the display technology.

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“…and M and N represent the lengths of the wavelet functions in the spatial domain and are integers, l > 0 and integer, and Θ = π K and −K ≤ r ≤ K and K > 0 is integer. This is a wavelet function that is centered at fc and scaled by a discrete parameter l and rotated by the discrete parameter r. The detailed working of the Morlet wavelet and its design is explained in [12]. The dot product of this wavelet function (in its scaled, rotated and translated form), and the hologram H results in a set of wavelet coefficients that approximate the hologram for a particular frequency and direction.…”

Section: Wavelet Analysismentioning

confidence: 99%

A framework for view-dependent hologram representation and adaptive reconstruction

Viswanathan

Gioia

Morin

2015

2015 IEEE International Conference on Image Processing (ICIP)

Self Cite

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In this paper, we present a complete framework for networked hologram adaptive transmission; we propose a well-suited wavelet basis allowing efficient local diffractive pattern extraction according to the user position, expose the relations between observer parameters and the pruning in the wavelet decomposition representation, and explain how the reconstruction is performed. The proposed framework has been validated on an experimental setup involving a kinect sensor for viewer position estimation.

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Morlet Wavelet transformed holograms for numerical adaptive view-based reconstruction

Cited by 13 publications

References 5 publications

Digital holography data compression

Digital holography data compression

Holographic representation: Hologram plane vs. object plane

A framework for view-dependent hologram representation and adaptive reconstruction

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