Image inpainting is the process of regenerating lost parts of the image. Supervised algorithm-based methods have shown excellent results but have two significant drawbacks. They do not perform well when tested with unseen data. They fail to capture the global context of the image, resulting in a visually unappealing result. We propose a novel self-supervised learning framework for image-inpainting: Weighted Self-Supervised Learning (WSSL) to tackle these problems. We designed WSSL to learn features from multiple weighted pretext tasks. These features are then utilized for the downstream task, image-inpainting. To improve the performance of our framework and produce more visually appealing images, we also present a novel loss function for image inpainting. The loss function takes advantage of both reconstruction loss and perceptual loss functions to regenerate the image. Our experimentation shows WSSL outperforms previous methods, and our loss function helps produce better results.
翻译:图像映射是重新生成图像丢失部分的过程。 受监督的算法方法显示出优异的结果, 但有两个显著的缺点。 当用不可见的数据测试时, 它们表现不佳 。 它们无法捕捉图像的全局背景, 从而产生一种视觉上不吸引的结果 。 我们为图像映射提出一个新的自我监督的学习框架: 加权自我监督学习( WSSSL) 来解决这些问题 。 我们设计了 WSSL 来从多重加权的借口任务中学习特征 。 这些功能随后被用于下游任务, 图像映射 。 为了改进框架的性能, 并产生更具有视觉吸引力的图像, 我们还为图像映射提供了一个新的损失功能 。 损失功能利用重建损失和感官损失功能来重新生成图像。 我们的实验显示 WSSL 超越了先前的方法, 我们的损失功能有助于产生更好的结果 。