With advancements in visual technology, an increasing number of visual techniques have recently been applied in other fields. Among them, mel spectrograms provide a bridge between audio features and visual models. Previous work has demonstrated that applying image processing methods to mel spectrograms is feasible. However, traditional image-based models operate at a relatively coarse level, focusing primarily on controlling texture and shape. In contrast, mel spectrograms are highly sensitive to detail, containing complex time-frequency information that requires more refined modeling. To address this, we propose MelRe, a visual model specifically designed for mel spectrograms, aimed at tackling complex fine-grained audio degradation issues from a visual perspective. MelRe addresses the need for fine-grained detail through pixel-level restoration methods and employs degradation alignment and noise simulation strategies to achieve high-precision restoration across varying levels of degradation, demonstrating exceptional restoration performance. Experimental results show that MelRe achieves a new state-of-the-art (SOTA) level in complex audio restoration tasks, highlighting its potential for high-quality audio repair.