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。

CLE 扩散：可控光增强扩散模型

CLE Diffusion: Controllable Light Enhancement Diffusion Model

In this work, we aim to reconstruct a time-varying 3D model, capable of
rendering photo-realistic renderings with independent control of viewpoint,
illumination, and time, from Internet photos of large-scale landmarks. The core
challenges are twofold. First, different types of temporal changes, such as
illumination and changes to the underlying scene itself (such as replacing one
graffiti artwork with another) are entangled together in the imagery. Second,
scene-level temporal changes are often discrete and sporadic over time, rather
than continuous. To tackle these problems, we propose a new scene
representation equipped with a novel temporal step function encoding method
that can model discrete scene-level content changes as piece-wise constant
functions over time. Specifically, we represent the scene as a space-time
radiance field with a per-image illumination embedding, where
temporally-varying scene changes are encoded using a set of learned step
functions. To facilitate our task of chronology reconstruction from Internet
imagery, we also collect a new dataset of four scenes that exhibit various
changes over time. We demonstrate that our method exhibits state-of-the-art
view synthesis results on this dataset, while achieving independent control of
viewpoint, time, and illumination.

本文旨在从互联网照片中重建一个可呈现照片逼真效果、拥有独立控制视角、照明和时间的三维模型。我们使用一种新的场景表示，并提出了一种新的时态步函数编码方法，可以将离散场景级内容变化建模为时间上的分段常函数，从而实现了对视角、时间和照明的独立控制。