相关实验视频

Updated: Sep 18, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

LEESDFormer:一个基于CNN-变压器的轻量级无监督曲线估计网络,用于低光图像增强,曝光抑制和无噪声

Rui He¹, Xuanhe Li¹, Jian Wu¹

¹National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.

Neural networks : the official journal of the International Neural Network Society

|June 26, 2025

概括

本研究介绍了LEESDFormer,这是一种使用新的CNN-Transformer架构进行低光图像增强的无监督方法. 它有效地解决了噪音和混合暴露,实现了卓越的视觉质量和细节保存.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
人工智能的人工智能

背景情况:

现有的低光增强方法在混合曝光和噪音方面扎,通常会丢失图像细节.
卷积神经网络 (CNN) 在捕获远程依赖和全球图像信息方面存在局限性.

研究的目的:

开发一种无监督的低光图像增强方法,克服当前方法的局限性.
引入一种新的CNN-Transformer架构,以改善低光图像增强和消噪.

主要方法:

提出一个低光增强和暴露抑制S形曲线 (LEES-S曲线) 以简化增强和暴露抑制.
使用CNN转换器架构 (LEESDFormer) 与低光图像增强和曝光抑制模块 (LEESM) 和图像拒绝模块 (IDM).

主要成果:

与最先进的无监督方法相比,LEESDFormer表现出优越的稳定性,概括性和视觉效果.
在LOL-v2真实数据集上实现了21dB的峰值信号噪声比 (PSNR),显示出强烈的增强和消除噪声.
该方法具有65K参数的效率,并在8ms内处理图像.

结论:

LEESDFormer在无人监督的低光图像增强和消除噪音方面取得了重大进展.
拟议的LEES-S曲线和CNN-Transformer架构有效地处理复杂的低光条件.
该方法的效率和性能使其适合在资源有限的设备上部署.

关键词:

曲线估计曲线的估计.暴露抑制可以抑制暴露.图像无效化图像无效化低亮度图像增强在低光条件下进行.无监督的基于CNN-变压器的网络.

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

相关概念视频

Calibration Curves: Linear Least Squares

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Reducing Line Loss

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.