<p>Convolutional neural networks are widely used for image reconstruction tasks such as pansharpening, low-light enhancement and super-resolution, which are often necessary preprocessing steps prior to downstream applications in remote sensing. However, a critical limitation of existing convolutional neural network architectures is the progressive loss of fine-grained spatial details as information propagates into deeper layers of the network. The degradation of these details restricts the network’s ability to restore high-fidelity images. To address this challenge, this paper introduces a novel Dense Residual Connection Mechanism (DRCM), which establishes multi-pathways for comprehensive feature reuse to effectively preserves more spatial details. We demonstrate the validity of DRCM by integrating it into several representative baseline networks, including PanNet, FusionNet, and DMDNet for pansharpening and super-resolution, and LLCNN, SICE, and RSCNN for low-light enhancement. Experimental evaluations on benchmark datasets, i.e., WorldView-3, WorldView-2 and SICE, reveal that our DRCM-based networks achieve enhanced performance, showing significant gains in spectral-spatial fidelity, structural detail preservation, and overall reconstruction accuracy. Crucially, these performance gains are realized with only a minor increase in model parameters and computational cost, underscoring DRCM’s high efficiency compared to increasing model parameters to reach similar accuracy. This work represents an architectural innovation for optimizing image enhancement accuracy without compromising efficiency in low-level vision applications.</p> Graphical Abstract <p></p>