关键词: 图像逆问题, 分析模型, 深度学习, 先验信息

Abstract:

Image inverse problems, which aims to reconstruct original images from degraded images or measurements observed by a given imaging system, is a hot topic in the recent development of signal processing. It has a wide range of application scenarios, such as compressed sensing, image denoising and image super-resolution etc. Recent years, the development of artificial intelligence has promoted the transformation of the field from analytical model-based methods to deep learning-based methods. However, whether in analytical model-based or deep learning-based methods, priors play a vital role in successfully solving image inverse problems. Different from existing related reviews which focus on introducing specific deep networks, this paper proposes a comprehensive and state-of-the-art review from a novel perspective of how to use priors in solving image inverse problems. After theoretical analysis and comparison of different methods, some future directions are finally put forward.

Key words: image inverse problem, analytical model, deep learning, priors