<p>Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degradation with a model. However, images often suffer from combinations of multiple degradation factors. Existing multiple-in-one IR models encounter challenges related to degradation diversity and prompt singularity when addressing such issues. In this paper, we propose a simple but effective multiple-in-one IR baseline model that can effectively restore images with both single and mixed degradations. To address degradation diversity, we design a Dynamic Filter Optimization (DFO) module which dynamically processes degraded areas of varying types and granularities. To tackle the prompt singularity issue, we develop an efficient Dual Attribute Embedding (DAE) module that guides the decoder in leveraging degradation-type and semantic related features, significantly improving the model’s performance in mixed degradation restoration scenarios. To validate the effectiveness of our model, we introduce a new challenging dataset containing both single and mixed degradation elements. Experimental results demonstrate that our proposed model achieves state-of-the-art (SOTA) performance, with a 1.5dB lead in PSNR on mixed degradation tasks and also on classic single-task restoration benchmarks.</p>