LMQFormer：一种用于轻量级去雪的拉普拉斯先验引导掩码查询Transformer
LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal
AI总结：
提出轻量高效去雪网络LMQFormer，通过Laplace-VQVAE生成粗糙掩码先验，设计MQFormer和DMQA模块降低计算成本并约束注意力区域，在主流数据集上以更少参数和最低运行时间达到最先进去雪质量。
摘要：
去雪旨在定位雪区域并恢复无修复痕迹的干净图像。与雨的规律性和半透明性不同，具有各种形态和退化程度的雪严重遮挡背景。因此，当前最先进的去雪方法通常保留较大的参数量。本文提出一种轻量但高效的去雪网络，称为Laplace Mask Query Transformer（LMQFormer）。首先，提出Laplace-VQVAE生成粗糙掩码作为雪的先验知识。不使用数据集中的掩码，旨在同时降低雪的信息熵和恢复的计算成本。其次，设计Mask Query Transformer（MQFormer）利用粗糙掩码去雪，采用两个并行编码器和一个混合解码器在轻量级需求下学习广泛的雪特征。第三，开发Duplicated Mask Query Attention（DMQA），将粗糙掩码转换为特定数量的查询，以减少参数并约束MQFormer的注意力区域。在主流数据集上的实验结果表明，所提模型在显著减少参数和最低运行时间下实现了最先进的去雪质量。
英文摘要：
Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-the-art snow removal quality with significantly reduced parameters and the lowest running time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
