Defocus Blur Detection via Salient Region Detection Prior

TL;DR

This paper introduces a novel defocus blur detection method that leverages salient region detection to overcome training data limitations, demonstrating improved performance through transfer learning.

Contribution

The paper proposes a transfer learning approach from salient region detection to defocus blur detection and introduces a new network architecture for better results.

Findings

Transfer learning improves defocus blur detection accuracy.

The proposed network outperforms existing models.

Salient region detection aids in defocus blur identification.

Abstract

Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision. Current works for defocus blur detection mainly focus on the designing of networks, the optimizing of the loss function, and the application of multi-stream strategy, meanwhile, these works do not pay attention to the shortage of training data. In this work, to address the above data-shortage problem, we turn to rethink the relationship between two tasks: defocus blur detection and salient region detection. In an image with bokeh effect, it is obvious that the salient region and the depth-of-field area overlap in most cases. So we first train our network on the salient region detection tasks, then transfer the pre-trained model to the defocus blur detection tasks. Besides, we propose a novel network for defocus blur detection. Experiments show that our transfer strategy works well on many current models, and demonstrate the superiority of our network.