Progressive and Selective Fusion Network for High Dynamic Range Imaging
This work improves HDR imaging for dynamic scenes, which is important for photography and computer vision applications, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of generating high dynamic range (HDR) images from low dynamic range (LDR) images in dynamic scenes with hand-held cameras, addressing issues like ghosting artifacts, and reports that the proposed method outperforms previous state-of-the-art methods on standard benchmarks.
This paper considers the problem of generating an HDR image of a scene from its LDR images. Recent studies employ deep learning and solve the problem in an end-to-end fashion, leading to significant performance improvements. However, it is still hard to generate a good quality image from LDR images of a dynamic scene captured by a hand-held camera, e.g., occlusion due to the large motion of foreground objects, causing ghosting artifacts. The key to success relies on how well we can fuse the input images in their feature space, where we wish to remove the factors leading to low-quality image generation while performing the fundamental computations for HDR image generation, e.g., selecting the best-exposed image/region. We propose a novel method that can better fuse the features based on two ideas. One is multi-step feature fusion; our network gradually fuses the features in a stack of blocks having the same structure. The other is the design of the component block that effectively performs two operations essential to the problem, i.e., comparing and selecting appropriate images/regions. Experimental results show that the proposed method outperforms the previous state-of-the-art methods on the standard benchmark tests.