Inspired by multi-task learning, degraded low-quality color-depth images enhancement tasks are transformed as a joint color-depth optimization model by using maximum a posteriori estimation. This model is optimized alternatively in an iterative way to get the solutions of CGD-SR task and Low-Brightness Color Image Enhancement (LBC-IE) task. The whole iterative optimization procedure is expanded as a joint model-driven unfolding network. Many experimental results have confirmed that high-resolution reconstruction of the depth map and the enhancement of low-brightness image can be realized simultaneously in one network. Furthermore, the proposed method with network interpretability can exceed that of many inexplicable CGD-SR methods and LBC-IE methods.