Abstract
Image enhancement methods often prioritize pixel level information, overlooking the semantic features. We propose a novel, unsupervised, fuzzy-inspired image enhancement framework guided by NSGA-II algorithm that optimizes image brightness, contrast, and gamma parameters to achieve a balance between visual quality and semantic fidelity. Central to our proposed method is the use of a pre trained deep neural network as a feature extractor. To find the best enhancement settings, we use a GPU-accelerated NSGA-II algorithm that balances multiple objectives, namely, increasing image entropy, improving perceptual similarity, and maintaining appropriate brightness. We further improve the results by applying a local search phase to fine-tune the top candidates from the genetic algorithm. Our approach operates entirely without paired training data making it broadly applicable across domains with limited or noisy labels. Quantitatively, our model achieves excellent performance with average BRISQUE and NIQE scores of 19.82 and 3.652, respectively, in all unpaired datasets. Qualitatively, enhanced images by our model exhibit significantly improved visibility in shadowed regions, natural balance of contrast and also preserve the richer fine detail without introducing noticable artifacts. This work opens new directions for unsupervised image enhancement where semantic consistency is critical.
Abstract (translated)
图像增强方法往往侧重于像素级别的信息，而忽视了语义特征。我们提出了一种新颖的、无监督的、受模糊理论启发的图像增强框架，该框架由NSGA-II算法引导，并优化图像亮度、对比度和伽马参数，以实现视觉质量和语义保真度之间的平衡。我们的方法核心在于使用一个预训练的深度神经网络作为特征提取器。为了找到最佳的增强设置，我们利用了GPU加速的NSGA-II算法，该算法在增加图像熵、提高感知相似性以及保持适当亮度等多重目标之间进行权衡。我们进一步通过应用局部搜索阶段来微调遗传算法产生的顶级候选者，从而改进结果。 我们的方法完全不需要配对训练数据，在标签有限或嘈杂的各种领域中具有广泛的应用潜力。从定量角度看，我们的模型在所有无配对的数据集中取得了优秀的性能，BRISQUE和NIQE的平均分数分别为19.82和3.652。从定性角度来看，经过我们模型增强后的图像在阴影区域的可见度得到了显著改善，对比度自然平衡，并且保留了更丰富的细节，同时未引入明显的伪影。 这项工作为无监督图像增强开辟了新的方向，在这种情况下，语义一致性至关重要。
URL
https://arxiv.org/abs/2505.11246
