论文标题
DILIE：图像增强的深度内部学习
DILIE: Deep Internal Learning for Image Enhancement
论文作者
论文摘要
我们考虑了将输入图像转换为感知看起来更好的图像的通用深度图像增强问题。图像增强的最新方法通过执行样式转移和图像恢复来考虑问题。这些方法主要分为两类：培训基于数据的和培训数据独立的（深度内部学习方法）。我们在深度内部学习框架中执行图像增强。我们对图像增强框架的深入内部学习增强了内容功能和样式特征，并使用上下文内容丢失来保存增强图像中的图像上下文。我们在朦胧和嘈杂的图像增强方面显示了结果。为了验证结果，我们使用结构相似性和感知误差，这有效地测量了图像中存在的不切实际变形。我们表明，所提出的框架的表现优于相关的最新作品，以增强图像。
We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider the problem by performing style transfer and image restoration. The methods mostly fall into two categories: training data-based and training data-independent (deep internal learning methods). We perform image enhancement in the deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework enhances content features and style features and uses contextual content loss for preserving image context in the enhanced image. We show results on both hazy and noisy image enhancement. To validate the results, we use structure similarity and perceptual error, which is efficient in measuring the unrealistic deformation present in the images. We show that the proposed framework outperforms the relevant state-of-the-art works for image enhancement.
