Raman lasers based on mid-infrared fibers operating at 3-5 µm atmospheric transparency window are attractive sources for several applications. Compared to fluoride and chalcogenide fibers, tellurite fibers are more advantageous for high power Raman fiber laser sources at 3-5 µm because of their broader Raman gain bandwidth, much larger Raman shift and better physical and chemical properties. Here we report on our simulations for the development of 10-watt-level 3-5 µm Raman lasers using tellurite fibers as the nonlinear gain medium and readily available continuous-wave (cw) and Q-switched erbium-doped fluoride fiber lasers at 2.8 µm as the pump sources. Our results show that a watt-level or even ten-watt-level fiber laser source in the 3-5 µm atmospheric transparency window can be achieved by utilizing the 1st- and 2nd-order Raman scattering in the tellurite fiber. The presented numerical study provides valuable guidance for future 3-5 um Raman fiber laser development.
In this paper, a novel scene-based nonuniformity correction (SBNUC) method using statistics between adjacent detectors for infrared focal-plane array (IRFPA) is proposed. Differential operation is designed as a decorrelation process to minimize the scene information remained into the nonuniformity parameters updating. Then, a global transfer matrix is established to construct the nonuniformity compensation matrix for the entire image. The adjacent differential statistics are demonstrated to be more effective and sufficient to estimate the nonuniformity when temporal variation is lacking. In addition, the proposed method exhibits the more superior capability of converging and does better in correcting all spatial components of nonuniformity. Application in simulated imagery and real infrared image shows the proposed method's excellent performance in convergence and elimination of ghosting artifacts.
Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.
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