Search

This paper presents a multi-frame super-resolution (SR) reconstruction algorithm based on diffusion tensor regularization term. Firstly, L1-norm structure is used as data fidelity term, anisotropic diffusion equation with directional smooth characteristics is introduced as a prior knowledge to optimize reconstruction result. Secondly, combined with shock filter, SR reconstruction energy functional is established. Finally, Euler-Lagrange equation based on nonlinear diffusion model is exported. Simulation results validate that the proposed algorithm enhances image edges and suppresses noise effectively, which proves better robustness.

The classical bilateral filtering algorithm is a non-linear and non-iterative image denoising method in spatial domain which utilizes the spatial information and the intensity information between a point and its neighbors to smooth the noisy images while preserving edges well. To further improve the image denoising performance, a spatially adaptive bilateral filtering image deonoising algorithm with low computational complexity is proposed. The proposed algorithm takes advantage of the local statistics characteristic of the image signal to better preserve the edges or textures while suppressing the noise. Experiment results show that the proposed image denoising algorithm achieves better performance than the classical bilateral filtering image denoising method.

Infrared zoom lens system with cooled focal plane array (FPA) detector is widely used in military application. Relevant information about optical design can be got easily, but research on infrared zoom lens system with low cost and high image quality for commercial application is less. This paper design a Compact infrared zoom lens system with only four lenses, using an uncooled focal plane array (FPA) with 384×288 pixels with zoom ratio 3:1. Because of large F number and less lenses, transmission of the whole zoom system is greatly improved. NETD and MRTD of infrared system will be satisfying by this compact design. There is no special surface such as diffractive surface, HOE in zoom lens system, and only Ge and Znse infrared materials are chosen which result in lower production cost of infrared zoom lens system for commercial applications.

With the development of the digital airborne photo-grammetry technology, the more performances of the optical system for airborne mapping camera are required, such as the longer focal, the wider field of view (FOV), at the same time, the secondary spectrum correction becomes more important and difficult for the optical system design. A high performance optical system of airborne mapping camera with 200mm focus and2ω=60°FOV is designed in this paper. The range of work wavelength is from 430nm to 885nm. A two-layer HDOE with negative dispersive characteristic is used to eliminate the secondary spectrum in the process of optical system design. The diffraction efficiency of the designed two-layer HDOE is up to 90%. From the result of design, the MTFs in whole fields are over 0.5 at 90lp/mm, which shows that the system has a great image quality. Meantime, the thermal analysis is done at the temperature range between -20°C and 40°C, and the MTF curves of the system at-20°C ~40°C show that a great image quality is kept, which meets the design requirements.

Automata and Petri nets are both used in research of discrete event systems (DES). Based on supervisory control theory, this paper provides an algorithm to convert automata into Petri nets in flexible manufacturing systems (FMS). The automaton to be transformed should satisfy three conditions: (1) it contains no self-loop, (2) its initial state is also the marker state, and (3) it is nonblocking. The automaton is first divided into several sub-automata by projecting it onto some groups of events. Second the redundant automata are deleted. Then the sub-automata remained are changed separately into places with transitions and arcs in Petri nets formalism. In Petri nets formalism of FMS, places are distinguished by idle, operation, and resource places. We set the initial tokens of idle and resource places to be n − 1 (n is the number of states corresponding to the automaton) and set the initial tokens of the operation places to be zero. Then the places and transitions are connected together to form a complete Petri net.