Multi-image encryption based on interference of computer generated hologram

A multi-image encryption scheme based on interference of computer generated holograms (CGH) is proposed. The encrypted information can be divided into several parts and recorded by corresponding CGHs that distribute randomly. With interference of all CGHs, the original information can be reconstructed. So the multi-image encryption is achieved, and every hologram can be regarded as the key to the corresponding image. Multi-user authentication and storage of information is implemented by applying unique CGH to interfere the common CGH. Furthermore, the CGHs can be cascaded to implement classification of images. When images of different level are assigned to corresponding user, hierarchical encryption is completed successfully. Numerical simulation verifies the feasibility of the method, and demonstrates the security of the algorithm and the decryption characteristics. Flexibility and variability of scheme can be higher than the existing methods. There are a lot of scheme's details still to consider and fulfill in the future.     

Multi-parameter measuring method of image intensifier based on Fourier transform phase measurement

A new quality parameter measuring method of image intensifier based on Fourier transform phase measurement is proposed. A standard sinusoidal fringe pattern is projected into the measuring system by spatial light modulator and the corresponding reference pattern and deformed pattern are captured by CCD. The Fourier transform and inverse Fourier transform are applied in order to extract the phase distribution of deformed pattern. The magnification, pillow-shaped distortion, barrel-shaped distortion, snake-shaped distortion and shear distortion of image intensifier are obtained at the same time. Experiment has proved its feasibility with real-time performance and without manual intervention.     

Fast image enhancement using multi-scale saliency extraction in infrared imagery

We propose a multi-scale saliency extraction based fast infrared image enhancement approach. A local frequency-tuned based saliency extraction technique is designed for highlighting the salient regions, firstly. Then, multi-scale saliency extraction is demonstrated, introducing multi-scale local windows with different sizes to extract regions of interest at different scales. Finally, the original image is enhanced with combining multi-scale salient image regions into one image. The experimental results prove the proposed approach is robust and efficient for infrared image enhancement.     

Two-dimensional Otsu's thresholding segmentation method based on grid box filter

Otsu algorithm, an automatic thresholding method, is widely used in classic image segmentation applications. In this paper, a novel two-dimensional (2D) Otsu thresholding algorithm based on local grid box filter is proposed. In our method, firstly by utilizing the coarse-to-fine idea, the 2D histogram is divided into regions by grid technique, and each region is used as a point to form a new 2D histogram, to which 2D Otsu thresholding algorithm and an improved particle swarm optimization (PSO) algorithm are applied to get the region number of the new 2D histogram threshold. Then on the result region, the mean of the 2D histogram is computed base on box filter, and the two algorithms are applied again to obtain the final threshold for the original image. Experimental results on real data show that the proposed algorithm gets better segmentation results than the traditional recursion Otsu algorithm. It significantly reduces the time of segmentation process and simultaneously has the higher segmentation accuracy.     

A hierarchical structure with improved OMP sparse representation used with face recognition

With the rapid development of the face recognition technology, more and more optical products are applied in people's real life. The recognition accuracy can be improved by increasing the number of training samples, but the colossal training samples will result in the increase of computational complexity. In recent years, sparse representation method becomes a research hot spot on face recognition. In this paper we propose an energy constrain orthogonal matching pursuit (ECOMP) algorithm for sparse representation to select the few training samples and a hierarchical structure for face recognition. We filter the training samples with ECOMP algorithm and then we compute the weights by all selected training samples. At last we find the closest recovery sample to the test sample. Simultaneously the experimental results in AR, ORL and FERET database also show that our proposed method has better recognition performance than the LRC and SRC_OMP method.     

Determination of the shape of a binary object of a square with a square hole by means of chosen initial phases

The binary object treated is a square with a square hole. We first use a set of initial phases to determine the symmetry of the object. The initial image has the gross shape of a square with a square hole, and preserves symmetry of the object. With another set of initial phases, the initial image has gross shape of a square with a hole. Sayre's equation is applied to refine this gross shape. After iterations, the result shows a square with a distorted square hole. These three squares have approximately same edge lengths. These squares are larger than the object. The holes are smaller than the hole of the object. The initial phases or the gross shapes can be used as starting phases or starting envelopes in other iterative algorithms.     

Basic oxygen furnace steelmaking end-point prediction based on computer vision and general regression neural network

End-point prediction is one of the most difficult problems in basic oxygen furnace (BOF) steelmaking process. To address this problem, some researchers have proposed some methods based on flame image processing and pattern classification. Because of the dynamically changing flame and real-time needs during the blowing process, there are still some issues that need to be solved. We propose a novel method based on accurate and fast multi flame features extraction and general regression neural network (GRNN). Firstly, flame images were acquired, and then the background of each image was removed via color similarity determination algorithm; secondly, color, texture, and boundary features were extracted; the fast and robust boundary and texture features were extracted by using the proposed methods, and these features were tested for their validity to the end-point prediction via comparing them with some other similar methods; finally, the prediction model was built using multi-features and GRNN. The experimental results demonstrated that it is accurate and fast to use the proposed method to the BOF end-point predict.     

Analysis of the features and reconstruction of a high resolution infrared image based on a multi-aperture imaging system

Infrared images of good quality are strictly important for such applications as targets detection, tracking and identifying. Traditional single aperture infrared imaging system brings in some defects for its imaging scheme. Multi-aperture imaging system shows promising characteristic of improving image quality and reducing size of optical instruments. We reconstruct a high resolution infrared image from the low resolution sub-images collected by the compact multi-aperture imaging system. A novel reconstruction method called pixels closely arrange (PCA) is proposed based on analyzing the compound eye imaging process, and this method is verified in a simulated 3D infrared scene to capture sub-images. An evaluation of the reconstructed image quality is presented to discuss the significant factors that affect the final result. Experimental results show that the PCA method can be efficiently applied to the multi-aperture infrared imaging system as long as the structure of the micro-lens array is specifically designed to be adaptive to the infrared focal plane array (IFPA).     

An image reconstruction algorithm based on Bayesian theorem for electrical resistance tomography

Electrical resistance tomography (ERT) is considered to be one of the most promising process tomography techniques for two-phase/multiphase flow measurement due to the advantages such as high speed, low cost and non-intrusive sensing. The iterative image reconstruction algorithm based on Bayesian theorem has been derived to solve the ERT inverse problem. It is taken into account, which is different to the existing ERT image reconstruction algorithms, including the prior probability of the permittivity distribution and the noise information in the measurement data. Both simulation and experimental data were carried out for typical two-phase flow regimes. The flow regimes are identified according to the reconstructed images of the Bayesian iteration method and conventional methods. Results obtained indicate that the Bayesian iteration method improves the reconstructed image quality with the traditional linear back projection (LBP) and reweighted least squares (RLS). Therefore, the Bayesian iteration method is suitable for identification of online two-phase flow regimes.     

Blind image deconvolution using sparse and redundant representation

This work devotes to the image deconvolution problem that restores clear image from its blurred and noisy measurements with little prior about the blur. A deconvolution method based on sparse and redundant representation theory is developed in this paper. It firstly represents the blur and image over different redundant dictionaries and imposes sparsity constraint to their representation coefficients respectively, then alternately estimates them using an iterative algorithm employing optimization technique. Experimental results on astronomical images show that the proposed method can achieve as good performance as the method requiring a known blur, which demonstrates its effectiveness.