摘要：

在非均匀光照环境下用户获取到的图像往往呈现亮度分布不均、细节丢失等特点。针对现有图像增强算法在处理非均匀光照图像时容易造成局部过度增强或增强不足等问题，提出一种融合特征注意力机制的非均匀光照图像增强算法(ULIEN)。通过学习非线性Gamma函数将非均匀光照图像映射为增强图像，引入亮度注意力图和通道注意力机制分别为图像不同的亮度区域和特征通道分配不同的学习权值，实现不同区域的图像增强。在训练过程中，ULIEN增强网络无需任何参考图像，通过一组无参考损失函数的设计驱动增强网络训练。实验结果表明，经所提算法增强后的图像在主观视觉方面能有效避免细节丢失、伪影、局部过增强或增强不足等问题，在BTMQI、ENIQA、TMQI、UNIQUE客观评价指标上分别可达3.727 0、1.109 6、0.903 0、0.755 7，相较于对照增强算法具有明显优势。

关键词: 图像增强, 非均匀光照图像, Gamma校正, 无监督学习, 注意力机制

Abstract:

In uneven lighting conditions, images acquired by users often exhibit uneven brightness distribution and loss of details. Existing image enhancement methods suffer from local over-or under-enhancement when working with low-illumination images affected by uneven illumination.This study proposes an uneven illumination image enhancement algorithm called ULIEN fused with a feature attention mechanism. ULIEN learns a nonlinear Gamma function to effectively map unevenly illuminated images to enhanced images. The network integrates a luminance attention map and channel attention mechanism to mitigate local over- or under-enhancement issues. These components assign varying learning weights to different luminance areas and feature channels within the image, enabling the network to focus on the enhancement process in different regions. The enhancement network by the ULIEN exhibits a simple structure and is trained using a set of reference-free loss functions, eliminating the need for any reference image. Experimental results demonstrate the effectiveness of the ULIEN in preserving details, avoiding artifacts, and mitigating issues of local over- or under-enhancement problems from a subjective perspective. Furthermore, the images enhanced by the ULIEN achieves scores of 3.727 0, 1.109 6, 0.903 0, and 0.755 7 in BTMQI, ENIQA, TMQI, and UNIQUE, respectively, showcasing clear advantages over other enhancement algorithms.

Key words: image enhancement, uneven illumination image, Gamma correction, unsupervised learning, attention mechanism