Improving cellular nuclei detection in histological images by combining super-resolution techniques and deep learning-based object detectors.

Digital pathology has enabled large-scale analysis of histological images. However, accurate detection of cellular nuclei remains challenging due to variability in morphology, staining, and especially image resolution. Existing object detection approaches often degrade when applied to low-resolution images, and current solutions typically address either multi-scale detection or image enhancement independently. In this work, we propose a hybrid framework that integrates super-resolution with a dual-branch detection strategy combining full-image and patch-based inference. This design leverages both global contextual information and localized high-detail analysis to improve detection robustness. The outputs of both branches are fused through a confidence-weighted mechanism followed by non-maximum suppression and clustering-based refinement. The proposed method was evaluated on the NuCLS dataset, demonstrating consistent improvements over baseline detection approaches. In particular, the combined workflow achieved up to a 20% increase in mAP@0.5-0.95 at higher confidence thresholds, achieving competitive performance compared to state-of-the-art methods while maintaining a lightweight architecture. These results highlight the effectiveness of integrating super-resolution and multi-scale detection strategies for improving nuclei detection in histopathological images.