Spatiotemporal Adaptive Nonuniformity Correction Based on BTV Regularization

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Abstract
The residual nonuniformity response, ghosting artifacts and over-smooth effects are the main defects of existing nonuniformity correction (NUC) methods. In this paper, a spatiotemporal feature based adaptive NUC algorithm with bilateral total variation (BTV) regularization is presented. The primary contributions of the innovative method are embodied in the following aspects: BTV regularizer is introduced to eliminate nonuniformity response and suppress ghosting effects. Spatiotemporal adaptive learning rate is presented to further accelerate convergence, remove ghosting artifacts and avoid oversmooth. Moreover, random projection based bilateral filter is proposed to estimate the desired target image more accurately and yield more details in actual scene. Experimental results validated that the proposed algorithm achieves outstanding performance upon whether simulated data or real world sequence.
Funding Information
  • National Natural Science Foundation of China (61674120, 61571338, U1709218, 61672131)
  • Fundamental Research Funds for the Central Universities of China (JBG161113, 300102328110)
  • Key Research and Development Plan of Shaanxi Province (2017ZDCXL-GY-05-01)

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