Digital image processing with quantum approaches

Digital image processing with quantum approaches

Roadmap on optical security

Postprint (author's final draft

Effect of Pixel Size and Scintillator on Image Quality of a CCD-Based Digital X-ray Imaging System.

The term“Digital X-ray Imaging refers to a variety of technologies that electronically capture x-ray images. Once captured the images may be electronically processed, stored, displayed and communicated. Digital imaging has the potential to overcome weaknesses inherent in traditional screen-film imaging, with high detection efficiency, high dynamic range and the capability for contrast enhancement. Image processing also makes possible innovative techniques such as computer-aided diagnosis, tomosynthesis, dual-energy imaging, and digital subtraction imaging. Several different approaches to digital imaging are being studied, and in some cases, have been developed and are being marketed. Common to all these approaches are a number of technological and medical issues to be resolved. One of the technological issues is the optimal pixel size for any particular image sensor technology. In general, the spatial resolution of the digital image is limited by the pixel size. Unfortunately while reducing pixel size improves spatial resolution this comes at the expense of signal to noise ratio (SNR). In a scintillator-charge-coupled device (CCD) system, the signal can be increased by improving the efficiency of the scintillator or by reducing noise. This study used a very low noise CCD to determine if image quality, as indicated by the modulation transfer function (MTF), the noise power spectrum (NPS) and the detective quantum efficiency (DQE), could be maintained while reducing pixel size. Two scintillators, one a commonly used radiographic screen the other a thallium doped cesium iodide scintillator, were used and the results compared. The results of this study show that image quality can be maintained as pixel size is reduced and that high DQE can be attained and maintained over a wide range of spatial frequencies with a well designed scintillator

Perfectly secure steganography: hiding information in the quantum noise of a photograph

We show that the quantum nature of light can be used to hide a secret message within a photograph. Using this physical principle we achieve information-theoretic secure steganography, which had remained elusive until now. The protocol is such that the digital picture in which the secret message is embedded is perfectly undistinguishable from an ordinary photograph. This implies that, on a fundamental level, it is impossible to discriminate a private communication from an exchange of photographs.Comment: 5 pages, 3 figures + appendix : 5 pages, 6 figure

Mammographic image restoration using maximum entropy deconvolution

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure

Large-Scale Optical Neural Networks based on Photoelectric Multiplication

Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N \gtrsim 10^6$) networks and can be operated at high (GHz) speeds and very low (sub-aJ) energies per multiply-and-accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ/MAC, suggests performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully-connected and convolutional networks. We also present a scheme for back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5, figures, 2 table