A LOCAL METRIC FOR DEFOCUS BLUR DETECTION BASED ON CNN FEATURE LEARNING

Abstract

Defocus blur detection is an important and challenging task in computer vision and digital imaging fields. Previous work on defocus blur detection has put a lot of effort into designing local sharpness metric maps. This paper presents a simple yet effective method to automatically obtain the local metric map for defocus blur detection, which based on the feature learning of multiple convolutional neural networks (ConvNets). The ConvNets automatically learn the most locally relevant features at the super-pixel level of the image in a supervised manner. By extracting convolution kernels from the trained neural network structures and processing it with principal component analysis, we can automatically obtain the local sharpness metric by reshaping the principal component vector. Meanwhile, an effective iterative updating mechanism is proposed to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. The experimental results demonstrate that our proposed method consistently performed better than previous state-of-the-art methods.

Existing System

Singular Value Decomposition, Linear Discriminant Analysis and Sparsity

Proposed System

In this paper, we proposed a simple yet powerful method to automatically obtain the local metric map for defocus blur detection, which based on the CNN feature learning. This method no longer requires laborious design of local metrics, and no longer need to know any prior information of the defocus image. We proposed a novel iterative updating mechanism to refine the defocus blur detection result from coarse to fine by exploiting the intrinsic peculiarity of the hyperbolic tangent function. Meanwhile, the experimental results show that we proposed method achieves the state-of-the-art performance and balances the detection accuracy and the detection time powerfully. 

Architecture

BLOCK DIAGRAM