The field of image processing and transmission is moving towards more efficient and robust methods. Researchers are exploring innovative approaches to reduce computational complexity and improve performance in resource-constrained applications. One notable direction is the use of learnable frameworks that incorporate side information, such as channel state information, to enhance semantic communication. Another area of focus is the development of lightweight models that can learn from limited data and achieve competitive results with heavyweight models. Noteworthy papers in this area include:

• Flyweight FLIM Networks for Salient Object Detection in Biomedical Images, which presents a novel network simplification method to reduce kernel redundancy and encoder size, resulting in very efficient SOD models.
• Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution, which improves model performance without increasing architectural complexity or inference costs and achieved state-of-the-art results in the NTIRE 2025 Efficient Super-Resolution Challenge.
• AdaptoVision: A Multi-Resolution Image Recognition Model for Robust and Scalable Classification, which introduces a novel CNN architecture that efficiently balances computational complexity and classification accuracy, achieving state-of-the-art results on several benchmark datasets.