Information Of Multiple Images Research Articles

Interference-based multiple-image encryption by phase-only mask multiplexing with high quality retrieved images

We propose a novel interference-based method for multiple-image encryption by phase-only mask (POM) multiplexing. The information of multiple images can be encrypted into two POMs (i.e. ciphertexts) without any iterative process. For correct decryption, one should hold the ciphertexts as well as the private keys, which are also POMs obtained analytically. Moreover, the bothersome silhouette problem can also be thoroughly resolved during the generation procedure of these POMs. The retrieved images by this method are totally free from the cross-talk noise that puzzles previous interference-based multiple-image encryption methods. Numerical results are presented to verify the validity of the proposed approach.

Multiple-image encryption and authentication with sparse representation by space multiplexing

A multiple-image encryption and authentication approach by space multiplexing has been proposed. The redundant spaces in the previous security systems employing sparse representation strategy are optimized. With the proposal the information of multiple images can be integrated into a synthesized ciphertext that is convenient for storage and transmission. Only when all the keys are correct can the information of the primary images be authenticated. Computer simulation results have demonstrated that the proposed method is feasible and effective. Moreover, the proposal is also proved to be robust against occlusion and noise attacks.

IMAGE SEQUENCE MEASURES FOR AUTOMATIC TARGET TRACKING

In the fleld of automatic target recognition and tracking, traditional image metrics focus on single images, ignoring the sequence information of multiple images. We show that measures extracted from image sequences are highly relevant concerning the performances of automatic target tracking algorithms. To compensate the current lack of image sequence characterization systems from the perspective of the target tracking di-culties, this paper proposes three new metrics for measuring image sequences: inter-frame change degree of texture, inter-frame change degree of target size and inter-frame change degree of target location. All are based on the fact that inter-frame change is the main cause interfering with target tracking in an image sequence. As image sequences are an important type of data in the fleld of automatic target recognition and tracking, it can be concluded that the work in this paper is a necessary supplement for the existing image measurement systems. Experimental results reported show that the proposed metrics are valid and useful.

Multi-frame Images Fusion with Image Enhancement for Different Focus Points

This paper presents a new approach to integrate and enhance registered multi-frame images with varied focuses simultaneously. The objective is to combine the information of multiple images from the same scene and then generate an image that is more suitable for human and machine perception or further image-processing tasks than its original sources. To enhance the integrated image, Smoothing Filter-based Intensity Modulation (SFIM) is first applied to multi-frame images with different focus points. Then the non-orthogonal wavelet decomposition based on contrast measure is used for extracting the details. Finally, the generated image is reconstructed by utilizing the inverse non-orthogonal wavelet transform. Experimental results demonstrate that the proposed approach can create a representation for the obtained scene with more details. Furthermore, this approach can achieve better performance than standard methods of discrete cosine transform (DCT), discrete wavelet transform (DWT), morphological wavelet transform (MWT), and Laplacian Pyramid.

NONITERATIVE 3D FACE RECONSTRUCTION BASED ON PHOTOMETRIC STEREO

3D face reconstruction is a popular area within the computer vision domain. 3D face reconstruction should ideally be achieved easily and cost-effectively, without requiring specialized equipment to estimate 3D shapes. As a result of this, many techniques for retrieving 3D shapes from 2D images have been proposed. In this paper, a novel method for 3D face reconstruction based on photometric stereo, which estimates the surface normal from shading information in multiple images, hence recovering the 3D shape of a face, is proposed. In order to overcome the problems of previous approaches related to prior-knowledge regarding lighting conditions and iterative algorithms, the exemplar is synthesized with known lighting conditions from at least three images, under arbitrary lighting conditions and using an illumination reference. Experiments in 3D face reconstruction were made by verifying the proposed approach using the illumination subset of the Max-Planck Institute face database and Yale face database B. Experimental results demonstrate that the proposed method is effective for 3D shape reconstruction of faces from 2D images.