TWO-STREAM MULTI-FOCUS IMAGE FUSION BASED ON THE LATENT DECISION MAP

The multi-focus image fusion with deep learning methods is mostly regarded as a two or three-category problem. Current systems utilize sliding windows to classify each pixel into focused or defocused, which is time consuming and requires post-processing such as denoising. In this paper, we propose a novel network architecture for multi-focus image fusion based on the latent decision map. For a regression task instead of a classification problem, we focus on learning the latent spatial decision map. This decision map indicates the degree of each focused pixel. To further improve the fusion result, we utilize the ResNet blocks to extract image features, and then combine low-level features with high-level semantic information. Our apporach makes the learning process easier and has better robustness and efficiency as well. Experimental results demonstrate that our framework has ability of achieving the state-of-the-art in terms of both qualitative and quantitative measures.