SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging

TL;DR

SAFNet is a novel efficient deep learning model for HDR imaging that selectively aligns and fuses multi-exposure images, achieving high quality results with significantly reduced computation and faster inference on resource-limited devices.

Contribution

The paper introduces SAFNet, a lightweight network with a selective alignment and fusion strategy, novel mask refinement, and a window partition cropping method for improved efficiency and quality in HDR imaging.

Findings

SAFNet outperforms previous state-of-the-art methods quantitatively and qualitatively.

SAFNet runs an order of magnitude faster than existing approaches.

The proposed methods effectively handle complex motion and large exposure differences.

Abstract

Multi-exposure High Dynamic Range (HDR) imaging is a challenging task when facing truncated texture and complex motion. Existing deep learning-based methods have achieved great success by either following the alignment and fusion pipeline or utilizing attention mechanism. However, the large computation cost and inference delay hinder them from deploying on resource limited devices. In this paper, to achieve better efficiency, a novel Selective Alignment Fusion Network (SAFNet) for HDR imaging is proposed. After extracting pyramid features, it jointly refines valuable area masks and cross-exposure motion in selected regions with shared decoders, and then fuses high quality HDR image in an explicit way. This approach can focus the model on finding valuable regions while estimating their easily detectable and meaningful motion. For further detail enhancement, a lightweight refine module is introduced which enjoys privileges from previous optical flow, selection masks and initial prediction. Moreover, to facilitate learning on samples with large motion, a new window partition cropping method is presented during training. Experiments on public and newly developed challenging datasets show that proposed SAFNet not only exceeds previous SOTA competitors quantitatively and qualitatively, but also runs order of magnitude faster. Code and dataset is available at https://github.com/ltkong218/SAFNet.