Souidene Wided, Abed-Meraim Karim, Beghdadi Azeddine
ESIGETEL. 77215 Avon-Fontainebleau.
IEEE Trans Image Process. 2009 Jul;18(7):1487-500. doi: 10.1109/TIP.2009.2018566. Epub 2009 May 12.
The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to estimate the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.
本文的目的是对医学图像盲识别(MBID)提出一种新的视角,研究一些已知方法,并提供一种用于恢复模糊和噪声图像的新的蒙特卡罗(MC)方法。首先,简要回顾直接图像恢复问题。然后提出一种基于逆滤波的新方法,用于无噪声情况下的完美图像恢复。通过在目标函数中引入正则化项来处理有噪声的情况,以避免噪声放大。其次,在蒙特卡罗背景下考虑滤波器识别问题。然后推导出一种估计退化矩阵滤波器的新的鲁棒解,并与全变差方法结合使用以恢复原始图像。提供了使用最新图像质量指标的仿真结果和性能评估,以评估所提方法的有效性。