Visual cryptography scheme with digital watermarking in sharing secret information from car number plate digital images

In this paper a visual cryptography scheme with a binary additive stream cipher is used to form the meaningless shares (share images or multiple layers) of original digital image, hiding some secret information. Each share image holds some information, but at the receiver side only when all of them are superimposed, the secret information is revealed by human vision without any complex computation. Proposed algorithm for generating shares is applied in MATLAB programming environment, using MATLAB built-in functions to create sequences of pseudorandom numbers or streams, which are used to make share images of original digital image. The input image is first converted into a binary image, shares are generated using pixel expansion scheme, and after that are sent to the receiver. At the received side, the shares could be printed in separate transparent sheets and overlapped in order to reveal the secret image, with some loss in contrast when compared to the original image. An algorithm is applied to car number plate digital images with watermark. Digital image watermarking method is used to embed some data in a car number plate digital image in order to verify the credibility of the content or the identity of the owner

Computational Optical Sensing and Imaging: feature issue introduction

This Feature Issue includes 19 articles that highlight advances in the field of Computational Optical Sensing and Imaging. Many of the articles were presented at the 2019 OSA Topical Meeting on Computational Optical Sensing and Imaging held in Munich, Germany, on June 24–27. Articles featured in the issue cover a broad array of topics ranging from imaging through scattering media, imaging round corners and compressive imaging to machine learning for recovery of images

Single-pixel imaging based on deep learning

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future