Abstract
The main objective of blind image de-blurring is to recover a sharp image from a given blurry image. A good estimation of the kernel plays an important role in recovering a sharp image. However, if the local object textures are neglected when the kernel is being estimated, this can lead to over-smoothing or can produce a strong ringing effect. In this paper, a new image regularization term based on the Probability Weighted Moments (PWM) for kernel estimation is proposed named as Probability Weighted Moments Regularization (PWMR). PWMR has the ability to preserve the small local texture structure in an image while minimizing the artifacts. Further, it can preserve the better contrast information between neighboring pixels and their corresponding central pixels in a current sliding window; moreover, it has the ability to resist outliers even in a small sample size. The kernel estimated by PWMR is subsequently used to recover the sharp latent image. An extensive comparison of synthetic and real standard benchmark images indicates the effectiveness of PWMR compared to current state-of-the-art blind image de-blurring methods.