Recent advances in camera design and imaging technology have enabled the capture of high-quality images using smartphones. However, due to the limited dynamic range of digital cameras, the quality of photographs captured in environments with highly imbalanced lighting often results in poor-quality images. To address this issue, most devices capture multi-exposure frames and then use some multi-exposure fusion method to merge those frames into a final fused image. Nevertheless, most traditional and current deep learning approaches are unsuitable for real-time applications on mobile devices due to their heavy computational and memory requirements. We propose a new method for multi-exposure fusion based on an encoder-decoder deep learning architecture with efficient building blocks tailored for mobile devices. This efficient design makes our model capable of processing 4K resolution images in less than 2 seconds on mid-range smartphones. Our method outperforms state-of-the-art techniques regarding full-reference quality measures and computational efficiency (runtime and memory usage), making it ideal for real-time applications on hardware-constrained devices. Our code is available at: https://github.com/LucasKirsten/MobileMEF.

翻译：近年来，相机设计与成像技术的进步使得智能手机能够拍摄高质量图像。然而，由于数码相机动态范围有限，在光照极度不均环境下拍摄的照片往往质量不佳。为解决这一问题，大多数设备会采集多曝光帧，然后采用某种多曝光融合方法将这些帧合并为最终融合图像。然而，大多数传统方法及当前深度学习方法因其较高的计算与内存需求，并不适用于移动设备上的实时应用。我们提出了一种基于编码器-解码器深度学习架构的多曝光融合新方法，该方法采用专为移动设备设计的高效构建模块。这种高效设计使我们的模型能够在中端智能手机上以低于2秒的速度处理4K分辨率图像。在全面参考质量指标与计算效率（运行时间与内存占用）方面，我们的方法均优于现有先进技术，使其成为硬件受限设备上实时应用的理想选择。我们的代码公开于：https://github.com/LucasKirsten/MobileMEF。