Abstract. An image detail enhancement algorithm is proposed based on in-place residual homogeneity (IP). Residual homogeneity is a physical law, which mainly explains the texture similarity between the same image residual at slightly different resolutions. As we all know, a single image can be divided into a base layer and a detail layer, and the effective estimation of the detail layer is the key in a detail enhancement algorithm. In the experiment, we find that the residual layer of an image obtained by bilinear interpolation is closely related to its detail layer, hence it can be used as the initial estimation of the detail layer, then residual homogeneity is applied to update the residual layer until the accurate detail layer is acquired. In the process of updating residuals, a searching method called fast in-place searching (FIPS) is used. FIPS only takes advantage of the residual homogeneity within the in-place region, which accelerates the project about 93%. Different from the local-based and global-based methods, our IP gets the detail layer directly and amplifies it. It has many good properties, such as being fast, edge-aware, robust, and parameter-free. Good performance has been demonstrated on several widely used datasets by both subjective and objective evaluations.

中文翻译：

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学习就地残差同质性以增强单幅图像细节

摘要。提出了一种基于原位残差均匀性(IP)的图像细节增强算法。残差同质性是一个物理定律，主要解释了在略有不同分辨率下相同图像残差之间的纹理相似性。众所周知，单个图像可以分为基础层和细节层，细节层的有效估计是细节增强算法的关键。在实验中，我们发现通过双线性插值得到的图像的残差层与其细节层密切相关，因此可以将其作为细节层的初始估计，然后应用残差均匀性来更新残差层，直到获取准确的细节层。在更新残差的过程中，使用了一种称为快速就地搜索（FIPS）的搜索方法。FIPS 仅利用就地区域内的残余同质性，这将项目加速了约 93%。与基于本地和基于全局的方法不同，我们的 IP 直接获取细节层并对其进行放大。它具有许多良好的特性，例如快速、边缘感知、鲁棒性和无参数。通过主观和客观评估，已经在几个广泛使用的数据集上证明了良好的性能。