Generative image watermarking enables the proactive detection and traceability of generated images. Among existing methods, inversion-based frameworks achieve highly conceal ed watermark embedding by injecting watermarks into the latent representation before the diffusion process. The robustness of this approach hinges on both the embedding mechanism and inversion accuracy. However, prior works have predominantly focused on optimizing the embedding process while overlooking inversion errors, which significantly affect extraction fidelity. In this paper, we address the challenge of inversion errors and propose ROAR, a dual-domain optimization-based framework designed to mitigate errors arising from two key sources: 1) Latent-domain errors, which accumulate across inversion steps due to inherent approximation assumptions. 2) Pixel-domain errors, which result from channel distortions such as JPEG compression. To tackle these issues, we introduce two novel components: A \textbf{Regeneration-based Optimization (RO)} mechanism, which incorporates an optimizable starting latent to minimize latent-domain errors; A Mixture of Experts (MoE)-based \textbf{distortion-adaptive restoration (AR)} network, which effectively recovers watermarked distributions from pixel-level distortions.Extensive experiments demonstrate that ROAR significantly reduces inversion errors and enhances watermark extraction robustness, thereby improving the reliability of generative image watermarking.