Blind retrospective shading correction using a multi-objective minimization criterion

In this paper, a fully automatic blind retrospective shading correction method based mainly on a minimization of a multi-objective criterion is presented. The proposed method assumes that the acquired image has distorted from a multiplicative and an additive shading component and thus can be adequately described by the linear image formation model. The estimation of the shading-free image is based on parametric estimation of the multiplicative and the additive shading component and the consequent application of the inverse image formation model. First of all, an initial estimation of the shading-free image is performed by the minimization of the multi-objective function of an appropriate image criterion. Secondly, the multiplicative and the additive shading components are estimated, based on assumptions about their frequency content and then, they median filtered. Finally, an estimation of a shading-free image is obtained using the above estimations for the components and the application of the inverse image formation model. Qualitative and quantitative experiments were conducted in a variety of image modalities including artificial and real images of finger, retinal images, transmission electron microscopy (TEM) images, arm, palm and hand vein images and thorax X-ray images. In all cases of distorted images, the proposed method had successfully removed the majority of the shading effects and had not distorted the shading-free images satisfying the main goal of retrospective shading correction. The signal to noise ratio (SNR) or equivalently the reciprocal of the coefficient of variations is used as a quantitative measure of the reduction/increase of intensity variations within the objects of the same class after shading correction. In our experiments, for the purpose of evaluation the signal to noise ratio is calculated for two different classes (object and background).