发表机构
School of Computer Science and Technology, Harbin Institute of Technology; School of Computer Science, Wuhan University（哈尔滨工业大学计算机科学与技术学院; 武汉大学计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对水下图像增强难题，提出聚类感知RWKV框架CRWKV，引入聚类感知语义动态重排序和暗响应调制局部传播，使WKV状态沿语义相关区域积累，补偿局部结构线索，在多个水下图像增强基准测试中取得最优性能。
AI 中文摘要
水下图像增强因光吸收、散射和后向散射导致颜色失真、对比度降低和细节丢失而具有挑战性。同一图像不同区域退化模式不同，需要区域自适应恢复。视觉RWKV模型虽能高效建模长程依赖，但预定义扫描顺序无法适应空间非均匀恢复需求。为此提出聚类感知RWKV框架CRWKV，引入聚类感知语义动态重排序（CSDR），根据语义特征相似性分组令牌并从簇间上下文关系推导动态遍历顺序，还提出暗响应调制局部传播（DMLP）补偿局部结构线索。实验表明CRWKV取得了最优的定量性能和视觉质量。
英文摘要
Underwater image enhancement remains challenging due to wavelength-dependent light absorption, scattering, and backscattering, which jointly cause color distortion, contrast degradation, and detail loss. Since these degradations vary with scene depth and imaging conditions, different regions within the same image often exhibit heterogeneous degradation patterns and thus require region-adaptive restoration. Although visual RWKV models offer an efficient linear-complexity solution for long-range dependency modeling, their predefined scanning orders are content-agnostic and therefore fail to adapt recurrent state propagation to spatially non-uniform restoration demands. To address this limitation, we propose a Clustering-aware RWKV framework, termed CRWKV, which reformulates the fixed recurrent propagation path of conventional RWKV into a content-adaptive token trajectory. Specifically, we introduce Clustering-aware Semantic Dynamic Reordering (CSDR), which groups tokens according to semantic feature similarity and derives a dynamic traversal order from inter-cluster contextual relations. This design enables WKV states to be accumulated along semantically correlated regions rather than fixed spatial or spectral orders. Since dynamic reordering may disrupt the local continuity of original spatial neighborhoods, we further propose Dark-response Modulated Local Propagation (DMLP), which extracts local structural responses via depth-wise convolution and adaptively modulates their propagation strength using a neighborhood-aware pseudo-dark response map. In this way, local structural cues are compensated before recurrent aggregation while preserving content-adaptive long-range modeling. Extensive experiments on multiple underwater image enhancement benchmarks demonstrate that CRWKV achieves state-of-the-art quantitative performance and superior visual quality.
Comments13 pages, 12 figures, IEEE Transactions on Image Processing journal paper, code available at https://github.com/geekpool/CLUIE. This paper presents CLUIE, a clustering-aware recurrent RWKV framework for spatially heterogeneous underwater image enhancement, with full-reference/no-reference quantitative comparisons, comprehensive ablation studies and feature visualization for CSDR and DMLP modules
