无需训练数据，用深度先验加L0梯度正则实现保边平滑
Deep Image Prior with L0 Gradient Regularizer for Image Smoothing
- 基于深度图像先验，引入L0梯度正则约束
- 在无训练数据下仍能有效保留边缘并去除纹理
- 适合图像去噪与压缩伪影修复场景
图像平滑是基础的图像处理操作，旨在保留重要结构如强边缘和轮廓，同时去除细微细节与纹理。传统方法依赖局部窗口统计或优化求解，近期先进方法虽采用深度学习，但需精心构建训练数据集。由于图像平滑数据集难以构造，本文提出DIP-ℓ₀框架，结合ℓ₀梯度正则化，可在无训练数据条件下实现高质量图像平滑。为有效最小化非凸、非光滑的ℓ₀损失函数，我们设计了一种基于ADMM的算法，利用现成的ℓ₀梯度最小化求解器。数值实验表明，DIP-ℓ₀在保边平滑和JPEG伪影去除方面优于多种现有算法。
原文摘要 · Abstract (English)
Image smoothing is a fundamental image processing operation that preserves the underlying structure, such as strong edges and contours, and removes minor details and textures in an image. Many image smoothing algorithms rely on computing local window statistics or solving an optimization problem. Recent state-of-the-art methods leverage deep learning, but they require a carefully curated training dataset. Because constructing a proper training dataset for image smoothing is challenging, we propose DIP-$\ell_0$, a deep image prior framework that incorporates the $\ell_0$ gradient regularizer. This framework can perform high-quality image smoothing without any training data. To properly minimize the associated loss function that has the nonconvex, nonsmooth $\ell_0$ ``norm", we develop an alternating direction method of multipliers algorithm that utilizes an off-the-shelf $\ell_0$ gradient minimization solver. Numerical experiments demonstrate that the proposed DIP-$\ell_0$ outperforms many image smoothing algorithms in edge-preserving image smoothing and JPEG artifact removal.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。