Error diffusion using linear pixel shuffling

Linear pixel shuffling error diffusion is a digital halftoning algorithm that combines the linear pixel shuffling (LPS) order of visiting pixels in an image with diffusion of quantization errors in all directions. LPS uses a simple linear rule to produce a pixel ordering giving a smooth, uniform probing of the image. This paper elucidates that algorithm. Like the Floyd-Steinberg algorithm, LPS error diffusion enhances edges and retains high-frequency image information. LPS error diffusion avoids some of the artifacts (“worm-s,” “tears,” and “checkerboarding”) often associated with the Floyd-Steinberg algorithm. LPS error diffusion requires the entire image be available in memory; the Floyd-Steinberg algorithm requires storage proportional only to a single scan line

Synchronization of spatiotemporal semiconductor lasers and its application in color image encryption

Optical chaos is a topic of current research characterized by high-dimensional nonlinearity which is attributed to the delay-induced dynamics, high bandwidth and easy modular implementation of optical feedback. In light of these facts, which adds enough confusion and diffusion properties for secure communications, we explore the synchronization phenomena in spatiotemporal semiconductor laser systems. The novel system is used in a two-phase colored image encryption process. The high-dimensional chaotic attractor generated by the system produces a completely randomized chaotic time series, which is ideal in the secure encoding of messages. The scheme thus illustrated is a two-phase encryption method, which provides sufficiently high confusion and diffusion properties of chaotic cryptosystem employed with unique data sets of processed chaotic sequences. In this novel method of cryptography, the chaotic phase masks are represented as images using the chaotic sequences as the elements of the image. The scheme drastically permutes the positions of the picture elements. The next additional layer of security further alters the statistical information of the original image to a great extent along the three-color planes. The intermediate results during encryption demonstrate the infeasibility for an unauthorized user to decipher the cipher image. Exhaustive statistical tests conducted validate that the scheme is robust against noise and resistant to common attacks due to the double shield of encryption and the infinite dimensionality of the relevant system of partial differential equations.Comment: 20 pages, 11 figures; Article in press, Optics Communications (2011

Analysis of the edge effect of error propagation in digital halftones

The purpose of this research is to study quantitatively edge enhancement effects of different digital halftone algorithms, especially on Error diffusion and Linear Pixel Shuffling algorithms. It has been known that by changing the amount of error and the spread of error when processing original images, different edge enhancement effects are observed. In many cases sometimes an unsymmetrical edge enhancement phenomenon occurs at the junction of different gray levels. A new metric for describing edge enhancement effects is introduced. This metric is shown to be consistent and reliable. In order to describe the unsymmetrical edge enhancement phenomenon in some cases, a new unsymmetrical metric is introduced. Both of these metrics are expected to provide useful quantitative measurements of edge enhancement effects in various different halftone algorithms

Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201

Virtual electro-photographic printer model

A halftone image in the computer is a bitmap matrix that contains either 0 or 1 , where 0 means the printer will not deposit any toner onto a paper and 1 means the printer will deposit some amount of toner onto a paper. The amount of toner that is put by the printer onto a paper for a given input signal pattern is characterized. The hypothesis was that the distribution of toner mass on the paper for a given input matrix pattern can be modeled with a toner point spread function, a toner transfer efficiency function, and a noise function. In order to study toner mass distribution printed on paper, it is necessary to develop an analytical technique for measuring the distribution of toner mass. The analytical technique used in this thesis is an optical analysis based on light transmitted through the printed sample. This analytical technique was calibrated against a gravimetric analysis. Linear relation between the optical analysis and gravimetric analysis indicates that the technique can be used for measuring spatial distribution of printed toner mass on a micro-scale. Guided by experimental measurements of toner mass distribution, a quantitative model of the three printer functions, the spread function, the toner delivery function, and the noise function, were characterized. These functions were used to construct a printer function that was used to compare the efficiency of different halftone patterns. The result of the printer model was extended to include the optical point spread function of the paper. This provided a complete printing model that simulated both physical and optical dot gain

A Simple and Robust Gray Image Encryption Scheme Using Chaotic Logistic Map and Artificial Neural Network

A robust gray image encryption scheme using chaotic logistic map and artificial neural network (ANN) is introduced. In the proposed method, an external secret key is used to derive the initial conditions for the logistic chaotic maps which are employed to generate weights and biases matrices of the multilayer perceptron (MLP). During the learning process with the backpropagation algorithm, ANN determines the weight matrix of the connections. The plain image is divided into four subimages which are used for the first diffusion stage. The subimages obtained previously are divided into the square subimage blocks. In the next stage, different initial conditions are employed to generate a key stream which will be used for permutation and diffusion of the subimage blocks. Some security analyses such as entropy analysis, statistical analysis, and key sensitivity analysis are given to demonstrate the key space of the proposed algorithm which is large enough to make brute force attacks infeasible. Computing validation using experimental data with several gray images has been carried out with detailed numerical analysis, in order to validate the high security of the proposed encryption scheme

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks