Abstract
Image restoration, which aims to recover high-quality images from their corrupted counterparts, often faces the challenge of being an ill-posed problem that allows multiple solutions for a single input. However, most deep learning based works simply employ l1 loss to train their network in a deterministic way, resulting in over-smoothed predictions with inferior perceptual quality. In this work, we propose a novel method that shifts the focus from a deterministic pixel-by-pixel comparison to a statistical perspective, emphasizing the learning of distributions rather than individual pixel values. The core idea is to introduce spatial entropy into the loss function to measure the distribution difference between predictions and targets. To make this spatial entropy differentiable, we employ kernel density estimation (KDE) to approximate the probabilities for specific intensity values of each pixel with their neighbor areas. Specifically, we equip the entropy with diffusion models and aim for superior accuracy and enhanced perceptual quality over l1 based noise matching loss. In the experiments, we evaluate the proposed method for low light enhancement on two datasets and the NTIRE challenge 2024. All these results illustrate the effectiveness of our statistic-based entropy loss. Code is available at this https URL.
Abstract (translated)
图像修复的目标是从损坏的图像中恢复高质量的图像，通常面临着一个具有单个输入多项式解的问题。然而，大多数基于深度学习的作品仅仅采用L1损失来以确定性的方式训练网络，导致预测过拟合，感知质量差。在本文中，我们提出了一种新方法，将重点从确定性的像素逐像素比较转变为统计视角，强调学习分布而不是单个像素值。核心思想是引入空间熵到损失函数中，以测量预测和目标之间的分布差异。为了使空间熵不同寻常，我们采用核密度估计（KDE）来近似每个像素具有与其邻居区域的具体强度值的概率。具体来说，我们将熵与扩散模型相结合，旨在实现与基于L1噪声匹配的损失相比的卓越准确性和感知质量的提高。在实验中，我们对所提出的方法在两个数据集上的低光增强进行了评估，以及NTIRE挑战2024。所有这些结果都说明了基于统计熵的熵损失的有效性。代码可在此处访问：https://www.xxx.com/
URL
https://arxiv.org/abs/2404.09735
