摘要
设备捕捉高分辨率图像的能力对图像处理提出了新的挑战,现有的低光图像增强算法多是针对低分辨率图像设计的,在处理高分辨率图像时,存在细节不清晰、颜色失真等问题。利用图像自身包含的纹理信息和颜色信息,提出了一种边缘和颜色信息引导的高分辨率低光照图像增强算法。为改善卷积神经网络局部特征学习的局限性,引入了边缘解码器,有助于捕获图像中远距离的关键信息,提高对边界语义信息的编码。此外,为处理高分辨率图像,在上下文注意力块中引入了稀疏注意力机制,集中关注图像中的重要信息,以有效减少噪声干扰。另一方面,颜色解码器有效利用了低光图像自身的色度线索,提升了颜色信息恢复的准确性。
The ability of the device to capture high-resolution images poses a new challenge to image processing,and most of the existing low-light image enhancement algorithms are designed for low-resolution images,and there are problems such as unclear details and color distortion when dealing with high-resolution images.Using the texture information and color information contained in the image itself,an edge and color information guided high-resolution low-light image enhancement algorithm is proposed.To improve the limitation of local feature learning of convolutional neural network,an edge decoder is introduced,which helps to capture the key information in the image at a long distance and improves the encoding of semantic information at the boundary.In addition,in order to deal with high-resolution images,a sparse attention mechanism is introduced in the context attention blocks,which focuses on the important information in the image and effectively reduces noise interference.On the other hand,the color decoder effectively utilizes the chromaticity cues of the low-light image itself to improve the accuracy of color information recovery.
作者
张玲
李振宇
ZHANG Ling;LI Zhenyu(School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems,Wuhan University of Science and Technology,Wuhan 430065,China)
出处
《计算机科学》
北大核心
2025年第6期297-305,共9页
Computer Science
基金
国家自然科学基金(61902286)
湖北省自然科学基金(2023AFB615)。
关键词
高分辨率
图像增强
低光照
High resolution
Image enhancement
Low light
