文献类型：期刊文献

英文题名：An Efficient Dehazing Method Using Pixel Unshuffle and Color Correction

作者：Jing, Hongyuan[1,3];Wang, Kaiyan[1,3];Zhu, Zhiwei[4];Chen, Aidong[2,3];Hong, Chen[3];Zhang, Mengmeng[2,3]

通讯作者：Jing, HY[1];Zhang, MM[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Multiagent Syst Res Ctr, 97 Beisihuan East Rd, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Robot, 4 Gongti North Rd, Beijing 10002, Peoples R China;[4]North China Elect Power Univ, Sch Control & Comp Engn, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, 4 Gongti North Rd, Beijing 10002, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2025

卷号：134

外文期刊名：SIGNAL PROCESSING-IMAGE COMMUNICATION

收录：;EI(收录号：20250417725147);Scopus(收录号：2-s2.0-85215379542);WOS:【SCI-EXPANDED(收录号:WOS:001404880500001)】；

语种：英文

外文关键词：Image dehazing; Color correction; Detail recover; Pixel unshuffle; Adaptive weight

摘要：Severe weather conditions such as haze and rainstorm will lead to serious degradation of observed images, which will influence the performance of advanced visual tasks such as target detection. However, most of the existing image processing methods focus on dehazing while overlooking the restoration of image color and details. In this paper, we found that the variance of the RGB three channels of a pixel at a certain point in an RGB image is related to its corresponding degree of color brightness through a large number of experiments, and propose an efficient dehazing method called PUCCNet, which utilizes Pixel Unshuffle and Color Correction to enhance image detail information and improve color saturation. We designed a Detail Recover Block (DRB) in the network to capture the details of the input image and focus on local details through the attention mechanism. In the highdimensional part of the network, a Depth Local Global Residual Block (DLGRB) is introduced, which can simultaneously handle local and global features, thereby enhancing the model's expressive capability, improving its generalization ability, and reducing the risk of overfitting. The network obtains local details through the attention mechanism, and makes the output image of higher quality through color correction, which is aligned with the human visual system. Extensive experiments on synthetic datasets and real-world datasets demonstrate that the proposed method outperforms existing state-of-the-art methods.