Background oriented system (BOS) technology is widely used for measuring flow field density information. The fingerprint information of high-speed target flow field can be calculated with the digital image correction (DIC) method. However, these traditional DIC algorithms are unable to obtain large-scale features of target flow fields and costing high computational complexity. To deal with those problems, a method combination of dual tree complex wavelet transform and optical flow (DTCWT-OF) is proposed. The proposed method adopts a much sparser gradient divergence regularization to obtain much more sparse statistical characteristics of the target flow field, and utilize the prior knowledge of the target flow field fingerprint information. Meanwhile, the reconstruction method is processed in the wavelet transform domain. Compared to traditional DIC methods, the experimental results show that the proposed method improves the SNR by 5dB and can achieve quasi real-time reconstruction.
Transmissive membrane diffractive optic can be used in space optical telescope or long-distance sensing to reduce the size and mass of imaging system. However, images of membrane diffractive imaging system are inevitably affected by diffraction efficiency and atmospheric propagation, which leads to low utilization ratio of light energy and image blur. Clarity of this degraded data is a challenging task. In this paper, an effective post-processing method based on total-scale Retinex (TSR) is proposed to address this problem. First, we design a self-adaptive scale surround function model that is guided by the optical transmission rate to estimate illumination component. Then, we embed the estimated-illumination into Retinex model for directly estimating the final clarity component. Moreover, the proposed method is capable of quasi real-time processing video using temporal coherence. Our method extends the multi-scale Retinex model while it avoids color artifacts in transmission model that can appear due to incorrect depth estimation. The experimental results show that our approach obtains better performance than the other state-of-art methods with respect to contrast enhancement and color-cast correction.
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