There is growing interest in the use of deep neural network
(DNN) based image denoising to reduce patient’s X-ray
dosage in medical computed tomography (CT). An effective
denoiser must remove noise while maintaining the texture
and detail. Commonly used mean squared error (MSE) loss
functions in the DNN training weight errors due to bias and
variance equally. However, the error due to bias is often more
egregious since it results in loss of image texture and detail.
In this paper, we present a novel approach to designing a loss
function that penalizes variance and bias differently. Our proposed
bias-reducing loss function allows us to train a DNN
denoiser so that the amount of texture and detail retained
can be controlled through a user adjustable parameter. Our
experiments verify that the proposed loss function enhances
the texture and detail in denoised images with only a slight
increase in the MSE