Many existing multi-scale approaches for remote sensing image dehazing typically employ a unidirectional strategy, progressing from coarse to fine scales. This approach often leads to poor dehazing performance because information from subsequent scales is not fully leveraged and estimation errors at coarser scales will propagate to finer scales. To overcome the shortcomings and enhance the performance of image dehazing, bidirectional mechanisms are introduced to our proposed lightweight multi-scale bidirectional network (MSB-Net). The submitted MSB-Net comprises three intra-scale branches and two inter-scale branches. In the intra-scale branches, Transformer-based UNet integrates non-local sparse aggregation blocks to enhance global information recovery and improve modeling efficiency by learning global features at different scales. In the inter-scale branches, convolutional neural network (CNN)-based UNet augmented with bidirectional feature propagation blocks (BFPB) is employed to adaptively aggregate the multi-scale features at different scales. Although the local feature enhancement block in the CNN-based UNet is central to emphasizing local features, BFPB effectively exploits information spanning various scales for reconstruction by allowing bidirectional information propagation in both coarse-to-fine and fine-to-coarse flows. Experimental results demonstrate that MSB-Net outperforms other popular Transformer-based approaches, particularly achieving a 16% reduction in model parameters.