论文标题
阴影感知的动态卷积用于删除阴影
Shadow-Aware Dynamic Convolution for Shadow Removal
论文作者
论文摘要
在许多收集的图像中，由于未经污染的图像对于许多下游多媒体任务至关重要，因此阴影的删除引起了人们的关注。当前的方法考虑了阴影和非阴影区域的相同卷积操作，同时忽略了阴影区域和非阴影区域的颜色映射之间的巨大差距，从而导致重建图像的质量差和沉重的计算负担。为了解决这个问题，本文介绍了一个新颖的插入插件的阴影感知动态卷积（SADC）模块，以使阴影区域与非阴影区域之间的相互依存关系解除。受到以下事实的启发：非阴影区域的颜色映射更易于学习，我们的SUDC以计算上便宜的方式以轻量级的卷积模块来处理非阴影区域，并使用更复杂的卷积模块恢复阴影区域，以确保图像重建的质量。鉴于非阴影区域通常包含更多背景颜色信息，因此我们进一步开发了一种新型的卷积内蒸馏损失，以增强从非阴影区域到阴影区域的信息流。在ISTD和SRD数据集上进行的广泛实验表明，我们的方法在许多最先进的情况下取得了更好的阴影去除性能。我们的代码可从https://github.com/xuyimin0926/sadc获得。
With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC.
