Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose encoder-free multiaxis physics-aware fusion network (EMPF-Net), a novel EMPF-Net that exhibits both light-weighted characteristics and computational efficiency. In our pipeline, we contend that conventional u-shaped networks allocate substantial computational resources to encode haze-degraded features, which play a subordinate role in the reconstruction process. Consequently, our encoder stages solely incorporate down-sampling operations. To improve the representation efficiency and enhance the generalization capabilities, we devise a multiaxis partial queried learning block (MPQLB) that primarily concentrates on learning dimension-wise queries, instead of relying solely on strictly correlated content of the input features. Furthermore, we augment the reconstruction procedure by incorporating ground truth supervision into each stage via a supervised cross-scale transposed attention module (SCTAM). It calculates attention maps under the guidance of clean images, thereby suppressing less informative features to propagate to the subsequent level. In addition, to address the challenge of ineffective intralevel feature fusion, which result in insufficient elimination of haze-degraded information and have a negative impact on the quality of reconstructed images, we introduce a physics-aware intralevel fusion module (PIFM). This module harnesses a physical inversion model to facilitate the intralevel feature interaction and alleviate the interference of dehazing-irrelevant information. Our proposed EMPF-Net is evaluated on 12 publicly available datasets, and the experimental results substantiate our superiority in terms of both metrical scores and visual quality, despite being equipped with a modest parameter count of 300k. Our approach is readily accessible at https://github.com/chdwyb/EMPF-Net .