Abstract
Synthesizing high quality saliency maps from noisy images is a challenging problem in computer vision and has many practical applications. Samples generated by existing techniques for saliency detection cannot handle the noise perturbations smoothly and fail to delineate the salient objects present in the given scene. In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images. DSAL-GAN consists of two generative adversarial-networks (GAN) trained end-to-end to perform denoising and saliency prediction altogether in a holistic manner. The first GAN consists of a generator which denoises the noisy input image, and in the discriminator counterpart we check whether the output is a denoised image or ground truth original image. The second GAN predicts the saliency maps from raw pixels of the input denoised image using a data-driven metric based on saliency prediction method with adversarial loss. Cycle consistency loss is also incorporated to further improve salient region prediction. We demonstrate with comprehensive evaluation that the proposed framework outperforms several baseline saliency models on various performance benchmarks.
Abstract (translated)
从噪声图像合成高质量的显著性图是计算机视觉中一个具有挑战性的问题，具有许多实际应用。现有的显著性检测技术所产生的样本不能很好地处理噪声扰动，不能很好地描述给定场景中的显著对象。针对噪声图像中的突出目标检测问题，提出了一种新的端到端耦合去噪显著性预测方法。dsal-gan由两个生成对抗网络（gan）组成，训练端到端以整体方式执行去噪和显著性预测。第一个GaN由一个对噪声输入图像进行去噪的发生器组成，在鉴别器中，我们检查输出是去噪图像还是地面真值原始图像。第二个gan利用基于显著性预测方法的数据驱动度量，利用输入去噪图像的原始像素预测显著性映射，并具有对抗性损失。同时，还引入了周期一致性损失，进一步提高了突出区域的预测能力。我们通过综合评估证明，所提出的框架在各种性能基准上优于多个基线显著性模型。
URL
https://arxiv.org/abs/1904.01215
