In this work, we present \(\text {denoi}\mathbb {S}\text {plit}\) , a method to tackle a new analysis task, i.e. the challenge of joint semantic image splitting and unsupervised denoising. This dual approach has important applications in fluorescence microscopy, where semantic image splitting has important applications but noise does generally hinder the downstream analysis of image content. Image splitting involves dissecting an image into its distinguishable semantic structures. We show that the current state-of-the-art method for this task struggles in the presence of image noise, inadvertently also distributing the noise across the predicted outputs. The method we present here can deal with image noise by integrating an unsupervised denoising subtask. This integration results in improved semantic image unmixing, even in the presence of notable and realistic levels of imaging noise. A key innovation in \(\text {denoi}\mathbb {S}\text {plit}\) is the use of specifically formulated noise models and the suitable adjustment of KL-divergence loss for the high-dimensional hierarchical latent space we are training. We showcase the performance of \(\text {denoi}\mathbb {S}\text {plit}\) across multiple tasks on real-world microscopy images. Additionally, we perform qualitative and quantitative evaluations and compare the results to existing benchmarks, demonstrating the effectiveness of using \(\text {denoi}\mathbb {S}\text {plit}\) : a single Variational Splitting Encoder-Decoder (VSE) Network using two suitable noise models to jointly perform semantic splitting and denoising.