Missing data poses challenges for machine learning applications across domains. Prevalent imputation techniques using deep learning have demonstrated limitations: GANs exhibit instability, while autoencoders tend to overfit. In real application scenarios, there are diverse types of missingness with varied missing rates, calling for an accurate and generic imputation approach. In this paper, we introduce dual corruption denoising autoencoders (DC-DAE). Our method involves two key innovations: firstly, augmenting inputs through dual corruptions during training, which prevents reliance on fixed missingness patterns and enhances generalization; secondly, employing a balanced loss function that provides control over the reconstruction of artificial missing data and the denoising of observed values. DC-DAE has a simple yet effective architecture without the complexity of attention mechanism or adversarial training. By combining corruption robustness and high-fidelity reconstruction, DC-DAE achieves both accuracy and stability. We demonstrate state-of-the-art performance on multiple tabular datasets with different missing rates, outperforming GAN, DAE, and VAE baselines under varied missingness scenarios. Our results highlight the importance of diverse and proper corruptions when designing models for imputation. The proposed plug-and-play approach offers an effective solution for ubiquitous missing data problems across domains.