The field of image and signal restoration is witnessing significant advancements with the development of innovative frameworks and techniques. A notable direction is the increasing use of self-supervised and multimodal approaches, which enable the effective restoration of degraded images and signals without requiring extensive pre-training or paired datasets. These methods are demonstrating superior performance and adaptability across various domains, including computer vision, biomedical imaging, and audio processing. Another area of focus is the integration of heterogeneous data sources and modalities, allowing for more robust and accurate predictions. Noteworthy papers in this regard include DeblurSDI, which proposes a zero-shot, self-supervised framework for blind image deconvolution, and MID, which presents a novel self-supervised multimodal iterative denoising framework. Additionally, OmniFuser and OmniField are introducing adaptive multimodal fusion and conditioned neural fields for robust spatiotemporal learning, respectively. These developments are expected to have a significant impact on various applications, including image and signal restoration, predictive maintenance, and air quality prediction.