Quantum-inspired squeeze-and-excitation in a time-conditioned U-Net for satellite image cloud removal.

Cloud occlusion severely restricts the use of optical satellite imagery for remote sensing applications, motivating the development of robust image restoration techniques. Current deep learning methods based on attention mechanisms and U-Net architectures have demonstrated superior results in cloud removal. However, these models rely on conventional feature recalibration techniques, which are insufficient to account for complex spatial correlations and long-range contextual dependencies. In this research, a hybrid quantum-classical image restoration framework for cloud removal in optical satellite data is proposed, which integrates Quantum Squeeze-and-Excitation (QSE) modules into a time-conditioned U-Net. The proposed model employs a diffusion-inspired noise-prediction objective, where sinusoidal timestep embeddings condition the network on varying noise levels, enabling stable and adaptive denoising. Classical squeeze-and-excitation blocks are replaced with parameterized quantum circuits that act as structured non-linear excitation functions, modeling inter-channel dependencies within a hybrid quantum-classical framework. The experimental study conducted on the RICE1 cloud removal dataset demonstrates that the QSE-enhanced U-Net consistently improves reconstruction quality compared to classical U-Net baselines in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) while maintaining stable training behavior. Additionally, tests were carried out on the EuroSAT dataset using artificially created Perlin noise-based cloud occlusions to assess cross-dataset transfer behavior. These findings provide a viable path for hybrid quantum-classical architectures in remote sensing image restoration by demonstrating that quantum-inspired channel attention can successfully improve feature recalibration in diffusion-style restoration networks.