CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal

Yu, Qianhao; Zheng, Naishan; Huang, Jie; Zhang, Feng

doi:10.1007/978-3-031-25063-7_14

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…There are many works on shadow removal and image restoration, such as semisupervised models with guidance [1,4,5,6,7,8,9,10,11,2,3], GAN's methods [12,13,14,15,16,17,18,19], and some unsupervised methods [20,21,22,23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Shadclips:When Parameter-Efficient Fine-Tuning with Multimodal Meets Shadow Removal

Zhang,

Xu,

Tang

et al. 2024

Preprint

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Segment Anything Model (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. However, it faced challenges when it came to distinguishing between shadows and their backgrounds. To address this, we proposed ShadClips, which consists of SAM-optimizer and SONet. It has dramatically enhanced SAM’s ability to segment shadow images, differentiating between the background and both soft and hard shadows adeptly. Due to its dependence on pixel point inputs, the SAM-Optimizer interface could do better. This method presents challenges, especially when dealing with long, extended shadows. To make the user experience more intuitive and effective, we incorporated the capabilities of CLIPs. Therefore, simple text descriptions like “A photo of a shadow” can be used to guide the SAM-Optimizer, allowing it to select the most relevant shadow mask from SAM’s comprehensive category list. Meanwhile, we introduce SONet to shadow removal. A large number of experiments on ISTD/SRD prove that the proposed method is effective and satisfactory. The source code of the ShadClips can be accessed from https://github.com/zhangbaijin/SAM-helps-Shadow.

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