Single-image Defocus Deblurring by Integration of Defocus Map Prediction Tracing the Inverse Problem Computation

TL;DR

This paper introduces a novel defocus deblurring network that uses defocus map prediction as a conditional guide, employing spatial modulation to improve deblurring quality over previous CNN-based methods.

Contribution

It proposes a three-part network with spatially dynamic feature modulation guided by defocus maps, enhancing deblurring performance beyond existing CNN approaches.

Findings

Outperforms state-of-the-art methods on public datasets.

Uses spatial modulation based on defocus maps for better feature adjustment.

Achieves superior quantitative and qualitative results.

Abstract

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 results show that our method can achieve better quantitative and qualitative evaluation performance than the existing state-of-the-art methods on the commonly used public test datasets.