用多个专家生成器提升水下图像修复质量，效果远超传统方法。
Expert Operational GANS: Towards Real-Color Underwater Image Restoration
- 设计多个专家生成器，分别专注不同画质区间修复。
- 在LSUI数据集上达25.16 dB PSNR，显著优于单生成器模型。
- 推理时用判别器选最优结果，适合复杂水下场景修复任务。
复杂光传播、散射和深度相关衰减导致水下图像出现多样失真，修复仍具挑战。传统基于GAN的方法因单一生成器难以覆盖全范围退化，表现受限。为此，本文提出xOp-GAN，采用多个专家生成器，每个仅训练于特定画质子集，从而在对应质量区间实现最佳修复。训练完成后，各生成器对输入图像进行修复，由判别器根据感知置信度选出最优结果。xOp-GAN是首个在回归任务推理中使用判别器的多生成器GAN模型。在基准数据集LSUI上的实验表明，其PSNR最高达25.16 dB，显著超越所有单回归模型，且模型复杂度更低。
原文摘要 · Abstract (English)
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.
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