Progressive and Selective Fusion Network for High Dynamic Range Imaging

TL;DR

This paper introduces a progressive and selective feature fusion network for HDR imaging from LDR images, effectively handling dynamic scenes and reducing artifacts through multi-step fusion and comparison operations.

Contribution

It proposes a novel multi-step feature fusion network with specialized blocks for comparing and selecting regions, improving HDR image quality from dynamic scenes.

Findings

Outperforms state-of-the-art methods on benchmark tests.

Effectively reduces ghosting artifacts in dynamic scenes.

Enhances feature fusion for better HDR reconstruction.

Abstract

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.