Compression of phase-only holograms with JPEG standard and deep learning

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed "JPEG + deep learning" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression

Digital Holography Data Compression

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

Optical field data compression by opto-digital means

Compression of optical field data is of interest due to the many applications where this kind of information processing is necessary. In particular, holographic recording has significant requirements in a high volume of both phase and amplitude data. We analyze and present a comparison between the performances of two lossy compression methods applied over optical field data: the optical scaling compression technique based on a virtual optical system that performs a scaling of the optical field data, and the JPEG format. In particular, we study the compression of optical fields data extracted from off-axis digital holograms. Our results show that optical scaling is better suited for the compression of the highly random phase information found in the optical field data of 3D diffuse objects. Data loss and volume reduction for each method are measured and compared.Facultad de Ciencias ExactasFacultad de IngenieríaCentro de Investigaciones Óptica

Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion

We propose a robust autofocus method for reconstructing digital Fresnel holograms. The numerical reconstruction involves simulating the propagation of a complex wave front to the appropriate distance. Since the latter value is difficult to determine manually, it is desirable to rely on an automatic procedure for finding the optimal distance to achieve high-quality reconstructions. Our algorithm maximizes a sharpness metric related to the sparsity of the signal’s expansion in distance-dependent waveletlike Fresnelet bases. We show results from simulations and experimental situations that confirm its applicability

Coefficients Quantization at Off-axis Digital Hologram Wavelet Compression

Digital holographic information is compressed for storage of 2D- or 3D-objects amplitude and phase distributions, fast transmission, analyzing and displaying of these data. In this paper features of application of wavelet transforms for off-axis digital holograms compression are considered. The combined technique based on zero and twin orders elimination, wavelet compression of the amplitude and phase components of obtained Fourier spectrum and further additional compression of wavelet coefficients by thresholding and quantization is analyzed. Numerical experiments on reconstruction of images from the compressed holograms are performed. The comparative analysis of applicability of various wavelets and additional quantization of coefficients is performed. Obtained results demonstrate possibility of 180 and more times compression using iterative and noniterative methods of coefficients quantization and threshold zeroing less 80% of wavelet coefficients. Keywords: digital holography, hologram compression, wavelets, quantization, thresholding, digital image processing

Optical image compression and encryption methods

International audienceOver the years extensive studies have been carried out to apply coherent optics methods in real-time communications and image transmission. This is especially true when a large amount of information needs to be processed, e.g., in high-resolution imaging. The recent progress in data-processing networks and communication systems has considerably increased the capacity of information exchange. However, the transmitted data can be intercepted by nonauthorized people. This explains why considerable effort is being devoted at the current time to data encryption and secure transmission. In addition, only a small part of the overall information is really useful for many applications. Consequently, applications can tolerate information compression that requires important processing when the transmission bit rate is taken into account. To enable efficient and secure information exchange, it is often necessary to reduce the amount of transmitted information. In this context, much work has been undertaken using the principle of coherent optics filtering for selecting relevant information and encrypting it. Compression and encryption operations are often carried out separately, although they are strongly related and can influence each other. Optical processing methodologies, based on filtering, are described that are applicable to transmission and/or data storage. Finally, the advantages and limitations of a set of optical compression and encryption methods are discussed

Wireless holographic image communications relying on unequal error protected bitplanes

Holography is considered to be one of the most promising techniques of goggle-free visualization of the nearfuture. We consider wireless transmission of digital holograms, which are partitioned into multiple bitplanes that are then independently encoded by a forward error correction (FEC) code for transmission over wireless channels. The coding rates of these bitplanes will be optimized at the transmitter for the sake of achieving an improved holographic peak signal-to-noise ratio (PSNR) at the receiver. Our simulation results show that up to 2.6 dB of Eb=N0 or 12.5 dB of PSNR improvements may be achieved, when employing a recursive systematic convolutional (RSC) code