  The restoration of images affected by blur and noise has been widely studied
and has broad potential for applications including in medical imaging
modalities like computed tomography (CT). Although the blur and noise in CT
images can be attributed to a variety of system factors, these image properties
can often be modeled and predicted accurately and used in classical restoration
approaches for deconvolution and denoising. In classical approaches,
simultaneous deconvolution and denoising can be challenging and often represent
competing goals. Recently, deep learning approaches have demonstrated the
potential to enhance image quality beyond classic limits; however, most deep
learning models attempt a blind restoration problem and base their restoration
on image inputs alone without direct knowledge of the image noise and blur
properties. In this work, we present a method that leverages both degraded
image inputs and a characterization of the system blur and noise to combine
modeling and deep learning approaches. Different methods to integrate these
auxiliary inputs are presented. Namely, an input-variant and a weight-variant
approach wherein the auxiliary inputs are incorporated as a parameter vector
before and after the convolutional block, respectively, allowing easy
integration into any CNN architecture. The proposed model shows superior
performance compared to baseline models lacking auxiliary inputs. Evaluations
are based on the average Peak Signal-to-Noise Ratio (PSNR), selected examples
of good and poor performance for varying approaches, and an input space
analysis to assess the effect of different noise and blur on performance.
Results demonstrate the efficacy of providing a deep learning model with
auxiliary inputs, representing system blur and noise characteristics, to
enhance the performance of the model in image restoration tasks.
