With the rapid advancement of optical imaging technologies, there is an increasing demand for higher resolution, better image quality, and more compact, cost-effective optical systems. In recent years, super-resolution imaging based on digital micromirror devices (DMDs) has attracted considerable attention for its ability to surpass the resolution limits of traditional optical systems. However, the integration of DMDs introduces challenges such as optical eccentricity and tilt-induced aberrations. To mitigate these issues, the design of DMD-based super-resolution imaging systems often requires additional optical lenses and complex surface types to correct aberrations and enhance imaging quality. While this improves accuracy, it reduces energy transmittance and significantly raises manufacturing costs. In this paper, we focus on the simplified design and image quality optimization of such systems, aiming to enhance system performance and imaging accuracy.

To meet the needs of simplified design and improved imaging quality in DMD-based infrared super-resolution systems, we examine the computational super-resolution imaging theory based on DMDs, analyze their working mechanisms and applications, and propose a simplified design approach. The original projection lens is then simplified using Zemax software, and the point spread function (PSF) of the modified system is obtained. Building on this, we develop a super-resolution restoration and image quality optimization model using T-L (TVAL3-Lucy Richardson), along with an algorithm that combines super-resolution reconstruction and image deblurring restoration. This algorithm improves both resolution and clarity in degraded images captured by the simplified system. Simulation, indoor target, and outdoor scene imaging experiments are conducted to validate the method's accuracy. The findings offer new insights for designing cost-effective DMD-based optical systems.

To validate the method, two images with a resolution of 512 pixel×512 pixel and two images with a resolution of 144 pixel×144 pixel are selected for simulation experiments (Fig. 8). Based on evaluation metrics (Table 3), the average peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) outperform other restoration methods, with PSNR exceeding 27 and SSIM exceeding 0.85. Compared to blurred images captured by the simplified system, PSNR improves by an average of 78%, and SSIM by 71%. The root mean square error (RMSE) decreases by 36% compared to blurred images captured by the simplified system. Indoor target imaging experiments (Fig. 11, Table 4) demonstrate significant improvements after restoration, with PSNR reaching 20.37 and SSIM rising to 0.77, representing improvements of 58.5% and 57.1%, respectively, over the original images. Meanwhile, RMSE drops to 27.46, a 27% reduction. Finally, outdoor scene imaging experiments (Fig. 13) demonstrate excellent image enhancement under various distance conditions. Compared to direct super-resolution methods, the proposed approach significantly enhances detail reproduction while maintaining overall imaging stability. The natural image quality evaluator (NIQE) values (Table 5) for two outdoor scenes decreases from 8.10 to 5.13 and from 7.75 to 4.09, representing reductions of 36.7% and 47.2%, respectively.

The proposed method effectively simplifies the structure of DMD-based infrared super-resolution imaging systems, reduces alignment complexity, improves infrared transmittance, and lowers production costs. By iteratively combining super-resolution reconstruction and deblurring, and analyzing convergence, optimal image enhancement is achieved. Theoretically, the TVAL3 algorithm uses total variation (TV) regularization to enable sparse reconstruction while preserving edges and textures, thus reducing artifacts. The Lucy-Richardson algorithm iteratively corrects blur and enhances image sharpness. In simulations, PSNR improves by an average of 78% and SSIM by 71%, compared to directly captured images. In indoor experiments, PSNR improves by 58.5% and SSIM by 57.1%. In outdoor scene experiments, the method effectively restores details compared to degraded images captured by the simplified system. The proposed restoration method improves resolution and quality, simplifies system structure, and reduces both cost and operational complexity, facilitating integration and deployment. It is well-suited for applications requiring high portability and low cost, such as portable security monitoring, spaceborne remote sensing, and field exploration.