  If light passes through the body tissues, focusing only on areas where
treatment needs, such as tumors, will revolutionize many biomedical imaging and
therapy technologies. So how to focus light through deep inhomogeneous tissues
overcoming scattering is Holy Grail in biomedical areas. In this paper, we use
deep learning to learn and accelerate the process of phase pre-compensation
using wavefront shaping. We present an approach (LoftGAN, light only focuses on
treatment needs) for learning the relationship between phase domain X and
speckle domain Y . Our goal is not just to learn an inverse mapping F:Y->X such
that we can know the corresponding X needed for imaging Y like most work, but
also to make focusing that is susceptible to disturbances more robust and
precise by ensuring that the phase obtained can be forward mapped back to
speckle. So we introduce different constraints to enforce F(Y)=X and H(F(Y))=Y
with the transmission mapping H:X->Y. Both simulation and physical experiments
are performed to investigate the effects of light focusing to demonstrate the
effectiveness of our method and comparative experiments prove the crucial
improvement of robustness and precision. Codes are available at
https://github.com/ChangchunYang/LoftGAN.
