DFS-Net: A Dense Focal Stack Image Generation Network from Misaligned Multi-Focus Images

Dense focal stack images inherently encode depth cues and are crucial for various 3D vision applications. However, existing generation methods are susceptible to misalignment and introduce a domain gap between synthetic and real-world data due to off-axis aberrations. To address these challenges, we introduce DFS-Net, an aberration-aware dense focal stack image generation network. DFS-Net consists of two core modules: all-in-focus image synthesis and aberration-aware point spread function (PSF) generation. The all-in-focus image synthesis is achieved through a densely connected fusion network based on multi-scale focus migration and focus property detection. This fusion network can effectively fuse misaligned multi-focus images into an all-in-focus image. The aberration-aware PSF generation is realized through a multi-layer perceptron (MLP) network. Supervised by ray-tracing-based PSFs, the MLP network can generate spatially varying PSFs for arbitrary spatial positions and focus distances. By selecting a set of focus distances, the generated PSF maps are locally convolved with the all-in-focus image to produce an aberration-aware dense focal stack. We conduct extensive comparative experiments on all-in-focus image fusion and focal stack generation against state-of-the-art methods. The experimental results demonstrate that DFS-Net can synthesize all-in-focus images with high subjective and objective quality, as well as generate dense focal stacks that closely approximate ray-tracing results. In addition, we conduct comparative experiments on the depth-from-focus and salient object detection tasks using the generated focal stacks. The experimental results demonstrate that our DFS-Net can significantly enhance the performance of existing depth-from-focus and salient object detection models. The code and dataset will be publicly available at https://github.com/North-Li/DFS-Net.