Title: BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS. Abstract: Encoder-decoder convolutional neural networks  (CNN)  have been extensively used for various  inverse problems. However,  their prediction error  for unseen test data is difficult to estimate  a priori, since the neural networks are trained using only selected data and their architectures are largely considered blackboxes. This poses a fundamental  challenge in improving the performance of  neural networks. Recently, it was shown that Stein’s unbiased risk estimator (SURE) can be used as an unbiased estimator of the prediction error for denoising problems. However, the computation of the divergence term in SURE is difficult to implement in a neural network framework, and the condition to avoid trivial identity mapping is not well defined. In this paper, inspired by the finding that  an encoder-decoder CNN can be expressed as a piecewise linear representation, we provide a  close form expression of  the unbiased estimator for the prediction error. The close form representation leads to a novel boosting scheme to prevent a neural network from converging to an identity mapping so that it can enhance the performance. Experimental results show that the proposed algorithm provides consistent improvement in various inverse problems.