A signal detection model for quantifying over-regularization in non-linear image reconstruction

Understand

Purpose: Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for non-linear image reconstruction.

• The vast majority of metrics employed for evaluating non-linear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE).
• Use of such metrics can lead to over-regularization in the sense that they can favor removal of subtle details in the image.
• To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details.