Infrared imaging faces a major challenge that dim, distant targets often appear as just a few blurry, noisy pixels, making them hard to be distinguished from background clutter. Traditional methods to enhance image resolution rely on sequences of low-resolution (LR) images. 

However, these sequences often suffer from time delays between frames and slight camera movements, introducing errors that limit the quality of the final high-resolution (HR) image. Besides existing super-resolution algorithms typically require pre-existing HR images for training, which are not available for novel, dim targets in real-world scenarios.

In a study published in Scientific Reports, researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences developed a new infrared imaging system that improves the clarity of dim, small targets often obscured by noise.

To solve the source error problem, researchers constructed a specialized Reflective Infrared Micro-Scanning Optical (RIMO) system which has a precisely controlled, high-speed vibrating mirror. The RIMO system generates multiple, perfectly aligned LR image frames in rapid succession. It eliminates the time lag and spatial displacement inherent in capturing separate images over time with a standard camera. This provides a clean sequence of LR images as input for the super-resolution process.

To address the algorithmic challenge, researchers developed a Self-Supervised Super Resolution Restoration (SSRR) algorithm. The SSRR algorithm works independently. It learns to estimate both the image blur and the potential HR image itself directly from the noisy, LR sequences provided by the RIMO system, without any external HR supervision. It employs a feature alignment network using deformable convolution to intelligently combine information from neighboring frames in the sequence, further enhancing its ability to reconstruct details and suppress noise.

The combined RIMO-SSRR system was rigorously tested. It was compared to traditional super-resolution techniques through simulations using both infrared and visible light cameras. Visual results demonstrated that images processed with the SSRR algorithm produced cleaner backgrounds and sharper, more defined targets. Objective measurement using Peak Signal-to-Noise Ratio (PSNR) confirmed that the SSRR algorithm achieved an over 16% higher PSNR than the traditional techniques, showing superior image fidelity and noise suppression.

This study holds promise for enhancing infrared detection capabilities. By enabling clearer imaging of tiny, dim targets, even in noisy conditions, the RIMO-SSRR system improves target tracking accuracy and overall situational awareness. The integration of specialized hardware with a powerful, self-taught algorithm represents a robust solution to a persistent challenge in infrared optoelectronics.