  Quotient regularization models (QRMs) are a class of powerful regularization
techniques that have gained considerable attention in recent years, due to
their ability to handle complex and highly nonlinear data sets. However, the
nonconvex nature of QRM poses a significant challenge in finding its optimal
solution. We are interested in scenarios where both the numerator and the
denominator of QRM are absolutely one-homogeneous functions, which is widely
applicable in the fields of signal processing and image processing. In this
paper, we utilize a gradient flow to minimize such QRM in combination with a
quadratic data fidelity term. Our scheme involves solving a convex problem
iteratively.The convergence analysis is conducted on a modified scheme in a
continuous formulation, showing the convergence to a stationary point.
Numerical experiments demonstrate the effectiveness of the proposed algorithm
in terms of accuracy, outperforming the state-of-the-art QRM solvers.
