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Salient object detection from low contrast images based on local contrast enhancing and non-local feature learning
The Visual Computer ( IF 3.0 ) Pub Date : 2020-08-29 , DOI: 10.1007/s00371-020-01964-9
Tengda Guo , Xin Xu

Salient object detection can facilitate numerous applications. Traditional salient object detection models mainly utilize low-level hand-crafted features or high-level deep features. However, they may face great challenges in the nighttime scene, due to the difficulties in extracting well-defined features to represent saliency information from low contrast images. In this paper, we present a salient object detection model based on local contrast enhancing and non-local feature learning. This model extracts non-local feature combines with local features under a unified deep learning framework. Besides, a deeply enhanced network is employed as a preprocessing of the low contrast images to assist our saliency detection model. The key idea of this paper is firstly hierarchically introducing a non-local module with local contrast-processing blocks, to provide a detailed and robust representation of saliency information. Then, an encoder-decoder image-enhanced network with full convolution layers is introduced to process the low contrast images for higher contrast and completer structure. As a minor contribution, this paper contributes a new dataset, including 676 low contrast images for testing our model. Extensive experiments have been conducted in the proposed low contrast image dataset to evaluate the performance of our method. Experimental results indicate that the proposed method yields competitive performance compared to existing state-of-the-art models.

中文翻译:

基于局部对比度增强和非局部特征学习的低对比度图像显着目标检测

显着物体检测可以促进许多应用。传统的显着目标检测模型主要利用低级手工特征或高级深度特征。然而,由于难以从低对比度图像中提取明确定义的特征来表示显着性信息,因此它们在夜间场景中可能面临巨大挑战。在本文中,我们提出了一种基于局部对比度增强和非局部特征学习的显着物体检测模型。该模型在统一的深度学习框架下结合局部特征提取非局部特征。此外,深度增强网络被用作低对比度图像的预处理,以帮助我们的显着性检测模型。本文的关键思想是首先分层引入具有局部对比度处理块的非局部模块,提供显着性信息的详细而可靠的表示。然后,引入具有全卷积层的编码器-解码器图像增强网络来处理低对比度图像以获得更高的对比度和更完整的结构。作为一个小贡献,本文提供了一个新的数据集,包括 676 个用于测试我们模型的低对比度图像。在提出的低对比度图像数据集中进行了大量实验,以评估我们方法的性能。实验结果表明,与现有的最先进模型相比,所提出的方法产生了具有竞争力的性能。引入了具有全卷积层的编码器-解码器图像增强网络来处理低对比度图像以获得更高的对比度和完整的结构。作为一个小贡献,本文提供了一个新的数据集,包括 676 个用于测试我们模型的低对比度图像。在提出的低对比度图像数据集中进行了大量实验,以评估我们方法的性能。实验结果表明,与现有的最先进模型相比,所提出的方法产生了具有竞争力的性能。引入了具有全卷积层的编码器-解码器图像增强网络来处理低对比度图像以获得更高的对比度和完整的结构。作为一个小贡献,本文提供了一个新的数据集,包括 676 张低对比度图像,用于测试我们的模型。在提出的低对比度图像数据集中进行了大量实验,以评估我们方法的性能。实验结果表明,与现有的最先进模型相比,所提出的方法产生了具有竞争力的性能。
更新日期:2020-08-29
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