An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

Abstract

Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image is to fuse several images focusing at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this paper, a novel α-matte boundary defocus model is proposed to generate realistic training data with the defocus spread effect precisely modeled, especially for areas near the FDB. Based on this α-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB. More specifically, the MMF-Net consists of two cascaded sub-nets for initial fusion and boundary fusion, respectively; these two sub-nets are designed to first obtain a guidance map of FDB and then refine the fusion near the FDB. Experiments demonstrate that with the help of the new α-matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.

arXiv comment10 pages, 8 figures, journal Unfortunately, I cannot spell one of the authors' name coorectly

References in corpus (4)

• Deep Visual Attention Prediction
• Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
• Unsupervised Deep Multi-focus Image Fusion
• Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network

Cited by in corpus (4)

• MFFW: A new dataset for multi-focus image fusion
• Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
• MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
• End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result