Abstract
The basic principle of the NAS-RIF algorithm is described and this algorithm has a simple structure, but it tends to amplify the noise and produce excessive smoothing in the iterative process. This paper takes some measures and focuses on overcoming these shortcomings. Firstly we use an adaptive denoising method based on total variation to reduce noise in turbulence-degraded images. Then, a regularized term is added into the cost function to preserve the edges effectively and a new form of the cost function is also improved to guarantee the convergence of the algorithm. Comparing with the traditional NAS-RIF algorithm, the proposed algorithm has a positive improvement in restraining the noise enlargement, preserving the detail features and the image restoration effect is obviously better.
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