GLA-ResUNet: A global-local attention framework for decoding in wavefront coding imaging system

Wavefront coding technology disperses incident laser energy by employing a pupil-phase mask, effectively reducing the energy density on the detector target and enhancing the camera’s anti-laser capability. Typically, phase masks with large modulation coefficients are used to achieve better protection performance, but this introduces significant challenges for subsequent digital restoration. The digital restoration process is a classical ill-posed problem, requiring simultaneous blur removal and noise suppression. Traditional image restoration methods often suffer from high-frequency noise amplification and processing artifacts. To address the challenge of restoring complex degraded images after encoding, a ResUNet model incorporating global and local attention mechanisms is constructed. This model synergistically extracts global contextual information and local detailed features of the image, overcoming limitations of traditional linear decoding methods, such as a strong dependence on noise priors and susceptibility to reconstruction artifacts. Simulations and experimental validations demonstrate that the proposed decoding algorithm effectively restores high-frequency image details and significantly improves reconstruction quality, outperforming conventional methods. Tests on real captured images at different distances show that the network model achieves optimal performance across multiple evaluation metrics, with an average PSNR of 21.75 dB, confirming its excellent image restoration capability. This research advances the practical application of wavefront encoding technology in laser protection, enabling systems to maintain high image quality while providing laser protection capabilities.