Adaptive defocus blurred image restoration based on fractional Fourier transform combining with clarity-evaluation-function

Abstract

The key issue to restore the defocus blurred image is how to choose a degradation model of blurred image. Based on the fractional Fourier transform (FrFT) combining with the clarity-evaluation-function, we present an approach for the restoration of defocus blurred image. This method constructs a defocused imaging model based on FrFT and estimates the lost phase signals from the intensity signals by an iterative phase retrieval approach, in which the sharp restored image can be acquired by implementing inverse FrFT on complex image signal made from the estimated phase signals and intensity signals. Using this model combing with the clarity-evaluation-function, the FrFT order can be changed adaptively. Consequently, the sharper image can be obtained in the end. Experimental results show the effectiveness, the robustness, and the low complexity of this approach, which make it more suitable for real-time environment.

A Novel Adaptive Underwater Image Biorthogonal Basic Wavelet Transform De-Noising Approach

“…However, the quality of images acquired using these techniques can be poor because the approximate parameters of the blurred image are unknown [5,6]. In some applications, such as bone age detection in medical inspection and selecting athletes, medical diagnosis, satellite imaging，estimating of blurred image extent and etc, image de-noising also plays an important role [7][8][9][10].…”