Existing table reasoning methods face challenges due to complex table structures and intricate logical reasoning. While supervised fine-tuning (SFT) dominates existing research, reinforcement learning (RL, such as Group Relative Policy Optimization: GRPO) has shown promise but struggled with low initial policy accuracy and coarse rewards in tabular contexts. In this paper, we introduce Table-R1, a three-stage RL framework that enhances multimodal table reasoning through: (1) Warm-up that prompts initial perception and reasoning capabilities, (2) Perception Alignment GRPO (PA-GRPO), which employs continuous Tree-Edit-Distance Similarity (TEDS) rewards for recognizing table structures and contents, and (3) Hint-Completion GRPO (HC-GRPO), which utilizes fine-grained rewards of residual steps based on the hint-guided question. Extensive experiments demonstrate that Table-R1 can boost the model's table reasoning performance obviously on both held-in and held-out datasets, outperforming SFT and GRPO largely. Notably, the Qwen2-VL-7B model with Table-R1 can surpass larger specific tabular reasoning open-source Large Vision Language Models (e.g., 13B Table-LLaVa), even achieving comparable performance to the closed-source model GPT-4o on held-in datasets. Our work highlights the efficacy of each stage of Table-R1 in overcoming initialization bottlenecks and reward sparsity, thereby boosting the GRPO on robust multimodal table reasoning for real-world applications.