  CT image quality is heavily reliant on radiation dose, which causes a
trade-off between radiation dose and image quality that affects the subsequent
image-based diagnostic performance. However, high radiation can be harmful to
both patients and operators. Several (deep learning-based) approaches have been
attempted to denoise low dose images. However, those approaches require access
to large training sets, specifically the full dose CT images for reference,
which can often be difficult to obtain. Self-supervised learning is an emerging
alternative for lowering the reference data requirement facilitating
unsupervised learning. Currently available self-supervised CT denoising works
are either dependent on foreign domain or pretexts are not very task-relevant.
To tackle the aforementioned challenges, we propose a novel self-supervised
learning approach, namely Self-Supervised Window-Leveling for Image DeNoising
(SSWL-IDN), leveraging an innovative, task-relevant, simple, yet effective
surrogate -- prediction of the window-leveled equivalent. SSWL-IDN leverages
residual learning and a hybrid loss combining perceptual loss and MSE, all
incorporated in a VAE framework. Our extensive (in- and cross-domain)
experimentation demonstrates the effectiveness of SSWL-IDN in aggressive
denoising of CT (abdomen and chest) images acquired at 5\% dose level only.
