Dropout the High-rate Downsampling：一种用于UHD图像恢复的新型设计范式
Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration
AI总结：
针对UHD图像恢复中高倍率下采样导致信息丢失和分块处理引入边界伪影的问题，提出D2Net范式，通过频域建立长距离依赖、多尺度卷积捕捉局部特征及动态融合编码特征实现全分辨率推理，在低光增强、去雾和去模糊任务上优于现有方法。
摘要：
随着高端移动设备的普及，超高清（UHD）图像在我们的生活中变得无处不在。由于像素数量庞大，UHD图像的恢复是一个非常具有挑战性的问题，在处理过程中经常导致内存溢出。现有方法要么在处理前对UHD图像进行高倍率下采样，要么将其分割成多个图像块进行单独处理。然而，高倍率下采样会导致显著的信息丢失，而基于图像块的方法不可避免地会引入边界伪影。在本文中，我们提出了一种新的设计范式来解决UHD图像恢复问题，称为D2Net。D2Net能够直接对UHD图像进行全分辨率推理，而无需进行高倍率下采样或将图像划分为多个图像块。具体而言，我们巧妙地利用频域特性来建立特征的长距离依赖关系。考虑到UHD图像中更丰富的局部模式，我们还设计了一个多尺度卷积组来捕捉局部特征。此外，在解码阶段，我们动态地融合来自编码阶段的特征，以减少无关信息的流动。在三个UHD图像恢复任务（包括低光照图像增强、图像去雾和图像去模糊）上的大量实验表明，我们的模型在定量和定性结果上均优于现有最先进的方法。
英文摘要：
With the popularization of high-end mobile devices, Ultra-high-definition (UHD) images have become ubiquitous in our lives. The restoration of UHD images is a highly challenging problem due to the exaggerated pixel count, which often leads to memory overflow during processing. Existing methods either downsample UHD images at a high rate before processing or split them into multiple patches for separate processing. However, high-rate downsampling leads to significant information loss, while patch-based approaches inevitably introduce boundary artifacts. In this paper, we propose a novel design paradigm to solve the UHD image restoration problem, called D2Net. D2Net enables direct full-resolution inference on UHD images without the need for high-rate downsampling or dividing the images into several patches. Specifically, we ingeniously utilize the characteristics of the frequency domain to establish long-range dependencies of features. Taking into account the richer local patterns in UHD images, we also design a multi-scale convolutional group to capture local features. Additionally, during the decoding stage, we dynamically incorporate features from the encoding stage to reduce the flow of irrelevant information. Extensive experiments on three UHD image restoration tasks, including low-light image enhancement, image dehazing, and image deblurring, show that our model achieves better quantitative and qualitative results than state-of-the-art methods.
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。
