Turbulent image deblurring using a deblurred blur kernel

Abstract

Abstract In the context of addressing a noisy turbulence-degraded image, it is common to use a denoising low-pass filter before implementing a deblurring algorithm. However, this filter not only suppresses noise but also induces a certain degree of blur into the degraded image. This blur effect causes a blurred estimate of the true blur kernel and ultimately leads to a distorted estimate of the latent clear image. To tackle this issue, this paper presents an innovative single-image deblurring method. It integrates a dedicated blur kernel deblurring step to mitigate the effects of the denoising filter. The L 0 norm and L 2 norm serve as the respective constraints for latent clear image and blur kernel. Experimental results on both synthetic and real-world turbulence-degraded images demonstrate the effectiveness and efficiency of the proposed method.