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 L0 norm and L2 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.