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Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation by Ye, Qian; Suganuma, Masanori; Okatani, Takayuki is a scholarly article available to read on EtoBox.

What is Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation about?

In this paper, we consider the problem in defocus image deblurring. Previous classical methods follow two-steps approaches, i.e., first defocus map estimation and then the non-blind deblurring. In the era of deep learning, some researchers have tried to address these two problems by CNN. However, the simple concatenation of defocus map, which represents the blur level, leads to suboptimal performance. Considering the spatial variant property of the defocus blur and the blur level indicated in the defocus map, we employ the defocus map as conditional guidance to adjust the features from the input blurring images instead of simple concatenation. Then we propose a simple but effective network with spatial modulation based on the defocus map. To achieve this, we design a network consisting of three sub-networks, including the defocus map estimation network, a condition network that encodes the defocus map into condition features, and the defocus deblurring network that performs spatially dynamic modulation based on the condition features. Moreover, the spatially dynamic modulation is based on an affine transform function to adjust the features from the input blurry images. Experimental

Author

Ye, Qian; Suganuma, Masanori; Okatani, Takayuki

Published

2022

Language

EN