Iterative Residual Image Deconvolution
This work addresses image deblurring for computer vision applications, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackles image deblurring by proposing an iterative residual deconvolution algorithm and a CNN architecture called CRCNet, which achieves better quantitative metrics and recovers more visually plausible texture details compared to state-of-the-art methods.
Image deblurring, a.k.a. image deconvolution, recovers a clear image from pixel superposition caused by blur degradation. Few deep convolutional neural networks (CNN) succeed in addressing this task. In this paper, we first demonstrate that the minimum-mean-square-error (MMSE) solution to image deblurring can be interestingly unfolded into a series of residual components. Based on this analysis, we propose a novel iterative residual deconvolution (IRD) algorithm. Further, IRD motivates us to take one step forward to design an explicable and effective CNN architecture for image deconvolution. Specifically, a sequence of residual CNN units are deployed, whose intermediate outputs are then concatenated and integrated, resulting in concatenated residual convolutional network (CRCNet). The experimental results demonstrate that proposed CRCNet not only achieves better quantitative metrics but also recovers more visually plausible texture details compared with state-of-the-art methods.