Traditional online class incremental learning assumes class sets in different tasks are disjoint. However, recent works have shifted towards a more realistic scenario where tasks have shared classes, creating blurred task boundaries. Under this setting, although existing approaches could be directly applied, challenges like data imbalance and varying class-wise data volumes complicate the critical coreset selection used for replay. To tackle these challenges, we introduce DECO (Dual-Enhanced Coreset Selection with Class-wise Collaboration), an approach that starts by establishing a class-wise balanced memory to address data imbalances, followed by a tailored class-wise gradient-based similarity scoring system for refined coreset selection strategies with reasonable score guidance to all classes. DECO is distinguished by two main strategies: (1) Collaborative Diverse Score Guidance that mitigates biased knowledge in less-exposed classes through guidance from well-established classes, simultaneously consolidating the knowledge in the established classes to enhance overall stability. (2) Adaptive Similarity Score Constraint that relaxes constraints between class types, boosting learning plasticity for less-exposed classes and assisting well-established classes in defining clearer boundaries, thereby improving overall plasticity. Overall, DECO helps effectively identify critical coreset samples, improving learning stability and plasticity across all classes. Extensive experiments are conducted on four benchmark datasets to demonstrate the effectiveness and superiority of DECO over other competitors under this online blurry class incremental learning setting.