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

提出了一种基于统计的熵损失方法，通过引入空间熵，强调对分布而非个别像素值的学习，利用核密度估计以使得熵可微分，并在实验证明了其在低光增强方面的有效性。

为低光图像增强提供可微分的空间熵的扩散模型装备

Equipping Diffusion Models with Differentiable Spatial Entropy for  Low-Light Image Enhancement

Low light enhancement has gained increasing importance with the rapid
development of visual creation and editing. However, most existing enhancement
algorithms are designed to homogeneously increase the brightness of images to a
pre-defined extent, limiting the user experience. To address this issue, we
propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion, a
novel diffusion framework to provide users with rich controllability. Built
with a conditional diffusion model, we introduce an illumination embedding to
let users control their desired brightness level. Additionally, we incorporate
the Segment-Anything Model (SAM) to enable user-friendly region
controllability, where users can click on objects to specify the regions they
wish to enhance. Extensive experiments demonstrate that CLE Diffusion achieves
competitive performance regarding quantitative metrics, qualitative results,
and versatile controllability. Project page:
https://yuyangyin.github.io/CLEDiffusion/

低光增强变得越来越重要；我们提出了一种能够提供丰富可控性的 Controllable Light Enhancement Diffusion Model（CLE Diffusion）扩散框架，利用条件扩散模型和照明嵌入来让用户控制所需的亮度水平，同时结合 Segment-Anything Model（SAM）进行用户友好的区域可控性，以实现出色的性能和多样化的可控性。