Deep learning-based joint source-channel coding has recently demonstrated strong potential for semantic communication (SemComm). However, most existing approaches focus on optimizing visual-fidelity metrics, which can lead to reduced perceptual quality. Generative model-based SemComm leverages rich prior knowledge from large-scale pre-training to enhance perceptual quality, but often at the cost of increased distortion and unreliability. This paper addresses the above issues by proposing a two-stage semantic image transmission framework, integrating a multimodal large language model (MLLM) for generative editing. In the first stage, a JSCC-based discriminative transmission selectively prioritizes semantically important regions, preserving scene layout and object integrity under limited bandwidth. In the second phase, MLLM-driven generative editing refines missing details based on the textual descriptions, enhancing semantic fidelity and perceptual quality. Extensive experiments show that the proposed framework achieves state-of-the-art performance in semantic preservation, perceptual quality, and visual fidelity across a wide range of channel conditions, especially in low-SNR regimes.