Single image deep defocus estimation and its applications

TL;DR

This paper presents a deep learning approach to estimate spatially varying defocus blur in images, enabling applications like image enhancement and multi-focus fusion by classifying blur levels with a neural network.

Contribution

The paper introduces a novel deep neural network-based method for classifying defocus blur levels in image patches and creating accurate defocus maps for various applications.

Findings

MobileNetV2 achieves high accuracy with low memory usage.

The method outperforms existing techniques in defocus estimation.

Applications include adaptive enhancement, magnification, and multi-focus fusion.

Abstract

Depth information is useful in many image processing applications. However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image. Extracting the depth information from the image is a challenging task. A guiding principle is that the level of blurriness due to defocus is related to the distance between the object and the focal plane. Based on this principle and the widely used assumption that Gaussian blur is a good model for defocus blur, we formulate the problem of estimating the spatially varying defocus blurriness as a Gaussian blur classification problem. We solved the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness. We have created a dataset of more than 500000 image patches of size $32\times32$ which are used to train and test several well-known network models. We find that MobileNetV2 is suitable for this application due to its low memory requirement and high accuracy. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel. We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement, defocus magnification, and multi-focus image fusion.