JSF：用于弱光图像增强的联合空频域网络

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computer Vision and Image Understanding Pub Date : 2025-09-16 DOI:10.1016/j.cviu.2025.104496

Yahong Wu , Feng Liu , Rong Wang

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引用次数: 0

摘要

弱光图像的增强一直是图像处理领域的一个突出热点。亮度对基于视觉的智能识别和分析有重要影响。本文在传统方法的基础上，提出了一种创新的用于弱光图像增强的联合空频域网络，称为JSF。在空间域中，通过融合全局和局部信息来优化亮度。在频域，利用傅里叶变换进行幅度和相位增强，降低噪声，放大细节。此外，通过线性和非线性拉伸融合了上述领域的增强结果。为了验证JSF的有效性，本文给出了定性和定量的比较结果，证明了它优于几种现有的最先进的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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JSF: A joint spatial-frequency domain network for low-light image enhancement

The enhancement of low-light images remains a prominent focus in the field of image processing. The degree of lightness significantly influences vision-based intelligent recognition and analysis. Departing from conventional methods, this paper proposes an innovative joint spatial-frequency domain network for low-light image enhancement, referred to as JSF. In the spatial domain, brightness is optimized through the amalgamation of global and local information. In the frequency domain, noise is reduced and details are amplified using Fourier Transformation to carry out amplitude and phase enhancement. Additionally, the enhanced results from the aforementioned domains are fused by linear and nonlinear stretching. To validate the effectiveness of JSF, this paper presents both qualitative and quantitative comparison results, demonstrating its superiority over several existing state-of-the-art methods.

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来源期刊

Computer Vision and Image Understanding 工程技术-工程：电子与电气

CiteScore

7.80

自引率

4.40%

发文量

112

审稿时长

79 days

期刊介绍： The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems