Inversion Algorithms Based on Deep Learning for Inverse Problems:Some Recent Progresses
Inverse problems are of wide and important applications in many areas such as radar and sonar,medical imaging,nondestructive testing and geophysical prospection.Inverse problems are ill-posed problems,so it is challenging to construct stable and highly effective inversion algorithms for them.One of the important methods to tackle this challenging issue is to devise an appropriate regularization strategy based on the a priori information of the unknown solution.The success of traditional regularization methods heavily depends on correctly encoding the a priori information of the unknown solution into the inversion algorithms,but this is in general very difficult in practical computation.With the development of deep learning techniques in recent years,it becomes possible to directly learn the a priori information of the unknown solutions of the inverse problems from data,which is helpful in developing highly effective and stable inversion algorithms.In this paper,we review some recent progres on inversion algorithms based on deep learning,focusing mainly on those based on learnable regularization framework.In addition,we also summarize the advantages and shortcomings of the inversion algorithms based on deep learning,and discuss their future research directions.
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