  Large Vision Model (LVM) has recently demonstrated great potential for
medical imaging tasks, potentially enabling image enhancement for sparse-view
Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of
data for training. Meanwhile, Deep Image Prior (DIP) effectively guides an
untrained neural network to generate high-quality CBCT images without any
training data. However, the original DIP method relies on a well-defined
forward model and a large-capacity backbone network, which is notoriously
difficult to converge. In this paper, we propose a stable optimization method
for the forward-model-free, LVM-based DIP model for sparse-view CBCT. Our
approach consists of two main characteristics: (1) multi-scale perceptual loss
(MSPL) which measures the similarity of perceptual features between the
reference and output images at multiple resolutions without the need for any
forward model, and (2) a reweighting mechanism that stabilizes the iteration
trajectory of MSPL. One shot optimization is used to simultaneously and stably
reweight MSPL and optimize LVM. We evaluate our approach on two publicly
available datasets: SPARE and Walnut. The results show significant improvements
in both image quality metrics and visualization that demonstrates reduced
streak artifacts. The source code is available upon request.
