Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration
Chen Zhang, Bangti Jin
备注：
this version is outdated, and we are completely reorganizing the paper and split it into several different pieces of work. Thus, we prefer to withdraw it from arxiv
Aleatoric uncertainty is an intrinsic property of ill-posed inverse and imaging problems. Its quantification is vital for assessing the reliability of relevant point estimates. In this paper, we propose an efficient framework for quantifying aleatoric uncertainty for deep residual learning and showcase its significant potential on image restoration. In the framework, we divide the conditional probability modeling for the residual variable into a deterministic homo-dimensional level, a stochastic low-dimensional level and a merging level. The low-dimensionality is especially suitable for sparse correlation between image pixels, enables efficient sampling for high dimensional problems and acts as a regularizer for the distribution. Preliminary numerical experiments show that the proposed method can give not only state-of-the-art point estimates of image restoration but also useful associated uncertainty information.
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Beijing University of Technology（北京工业大学）；Beijing Key Laboratory of Computational Intelligence and Intelligent System（计算智能与智能系统北京市重点实验室）；The Hong Kong Polytechnic University（香港理工大学）
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