4,737 research outputs found
Compressive Direct Imaging of a Billion-Dimensional Optical Phase-Space
Optical phase-spaces represent fields of any spatial coherence, and are
typically measured through phase-retrieval methods involving a computational
inversion, interference, or a resolution-limiting lenslet array. Recently, a
weak-values technique demonstrated that a beam's Dirac phase-space is
proportional to the measurable complex weak-value, regardless of coherence.
These direct measurements require scanning through all possible
position-polarization couplings, limiting their dimensionality to less than
100,000. We circumvent these limitations using compressive sensing, a numerical
protocol that allows us to undersample, yet efficiently measure
high-dimensional phase-spaces. We also propose an improved technique that
allows us to directly measure phase-spaces with high spatial resolution and
scalable frequency resolution. With this method, we are able to easily measure
a 1.07-billion-dimensional phase-space. The distributions are numerically
propagated to an object placed in the beam path, with excellent agreement. This
protocol has broad implications in signal processing and imaging, including
recovery of Fourier amplitudes in any dimension with linear algorithmic
solutions and ultra-high dimensional phase-space imaging.Comment: 7 pages, 5 figures. Added new larger dataset and fixed typo
Symmetries, inversion formulas, and image reconstruction for optical tomography
We consider the image reconstruction problem for optical tomography with diffuse light. The associated inverse scattering problem is analyzed by making use of particular symmetries of the scattering data. The effects of sampling and limited data are analyzed for several different experimental modalities, and computationally efficient reconstruction algorithms are obtained. These algorithms are suitable for the reconstruction of images from very large data sets
Hybrid DWT-DCT algorithm for image and video compression applications
Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multiresolution transformation.
In this work, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmark, medical and endoscopic images. Several QCIF, high definition, and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme.
The simulation results show that the proposed hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT, and WHT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The new scheme reduces “false contouring” and “blocking artifacts” significantly. The rate distortion analysis shows that for a fixed level of distortion, the number of bits required to transmit the hybrid coefficients would be less than those required for other schemes Furthermore, the proposed algorithm is also compared with the some existing hybrid algorithms. The comparison results show that, the proposed hybrid algorithm has better performance and reconstruction quality. The proposed scheme is intended to be used as the image/video compressor engine in imaging and video applications
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