标题： 专家操作生成对抗网络：面向真实颜色水下图像修复 显示英文标题

标题： Expert Operational GANS: Towards Real-Color Underwater Image Restoration

摘要： 从复杂光传播、散射和深度相关衰减中产生的广泛变形伪影使得水下图像恢复仍然是一个具有挑战性的问题。 与其他单个深度回归网络一样，传统的基于GAN的恢复方法在这一异构领域中表现不佳，因为单一的生成器网络通常不足以捕捉全部范围的视觉退化。 为了克服这一限制，我们提出了xOp-GAN，一种具有多个专家生成器网络的新GAN模型，每个生成器仅在特定图像质量的子集上进行训练。 因此，每个生成器可以学习在其特定质量范围内最大化其恢复性能。 一旦xOp-GAN训练完成，每个生成器都可以恢复输入图像，然后可以根据其感知置信度分数由判别器选择最佳恢复图像。 结果表明，xOP-GAN是第一个在回归任务推理过程中使用判别器的多生成器GAN模型。 在基准大规模水下图像（LSUI）数据集上的实验结果表明，xOp-GAN实现的PSNR水平高达25.16 dB，即使在复杂度降低的情况下，也大大超越了所有单回归器模型。 显示英文摘要

摘要： The wide range of deformation artifacts that arise from complex light propagation, scattering, and depth-dependent attenuation makes the underwater image restoration to remain a challenging problem. Like other single deep regressor networks, conventional GAN-based restoration methods struggle to perform well across this heterogeneous domain, since a single generator network is typically insufficient to capture the full range of visual degradations. In order to overcome this limitation, we propose xOp-GAN, a novel GAN model with several expert generator networks, each trained solely on a particular subset with a certain image quality. Thus, each generator can learn to maximize its restoration performance for a particular quality range. Once a xOp-GAN is trained, each generator can restore the input image and the best restored image can then be selected by the discriminator based on its perceptual confidence score. As a result, xOP-GAN is the first GAN model with multiple generators where the discriminator is being used during the inference of the regression task. Experimental results on benchmark Large Scale Underwater Image (LSUI) dataset demonstrates that xOp-GAN achieves PSNR levels up to 25.16 dB, surpassing all single-regressor models by a large margin even, with reduced complexity.