Conditional Laplacian pyramid networks for exposure correction

作     者：Huang, Mengyuan Chang, Kan Qin, Qingpao Tang, Yahui Li, Guiqing 

作者机构：Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Guangxi Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China 

出 版 物：《SIGNAL PROCESSING-IMAGE COMMUNICATION》 (Signal Process Image Commun)

年 卷 期：2025年第134卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foun-dation of China (NSFC) Guangxi Key RD Pro-gram [AB23075106] Guangxi Key Laboratory of Multi-media Communications and Network Technology 

主　　题：Exposure correction Low-light image enhancement Laplacian pyramid Convolutional neural network 

摘      要：Improper exposures greatly degenerate the visual quality of images. Correcting various exposure errors in a unified framework is challenging as it requires simultaneously handling global attributes and local details under different exposure conditions. In this paper, we propose a conditional Laplacian pyramid network (CLPN) for correcting different exposure errors in the same framework. It applies Laplacian pyramid to decompose an improperly exposed image into a low-frequency (LF) component and several high-frequency (HF) components, and then enhances the decomposed components in a coarse-to-fine manner. To consistently correct a wide range of exposure errors, a conditional feature extractor is designed to extract the conditional feature from the given image. Afterwards, the conditional feature is used to guide the refinement of LF features, so that a precisely correction for illumination, contrast and color tone can be obtained. As different frequency components exhibit pixel-wise correlations, the frequency components in lower pyramid layers are used to support the reconstruction of the HF components in higher layers. By doing so, fine details can be effectively restored, while noises can be well suppressed. Extensive experiments show that our method is more effective than state-of-the-art methods on correcting various exposure conditions ranging from severe underexposure to intense overexposure.