Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications.

The demand for high-spatial-resolution optical remote sensing applications is increasing, while conventional high-resolution optical payloads face limitations in widespread application due to their large size and high manufacturing costs. With the rapid development of image processing technology, we adopt a method combining small-pixel detector sampling with image deblurring algorithms to obtain high-spatial-resolution remote sensing images. In this work, we use Zernike polynomials to simulate diffraction-blurred small-pixel images under various aberration modulations, ensuring the simulation data follow solid physical principles. Furthermore, we propose a new weighted sparse model ℓ_we that combines the Welsch-weighted ℓ₁-norm with ℓ₀-norm constraints, and further applies ℓ_we regularization to both gradient fidelity terms and image gradient terms to enhance fidelity constraints and improve latent structure preservation. Compared with other sparse models, our model produces results with fewer residual structures and stronger sparsity. Comprehensive evaluations on both simulated small-pixel remote sensing datasets and real-world remote sensing images demonstrate that the proposed weighted sparse image quality restoration algorithm achieves more desirable results with excellent robustness. Compared to other methods, the proposed approach improves PSNR by an average of 2.5% and SSIM by 2.2%, while reducing ER by 20.7%. This provides an effective technical solution for image quality restoration of small-pixel remote sensing data.

Polarization-encoded detector multiplexing has recently shown promise for compact wide-field infrared imaging, where multiple field-of-view (FOV) regions are mapped onto a shared detector area and distinguished through Stokes-vector decoding. The practical implementation of this architecture, however, requires reflective folding components for optical path compression and detector reuse. Oblique metallic reflection introduces unequal amplitude attenuation and phase retardation between the p- and s-polarized components, which can deform the designed polarization code before it reaches the polarization-resolved detector. This work establishes a coordinate-consistent Jones–Mueller model for eight peripheral reflections from the internal octagonal mirror and one central direct path. The model distinguishes deterministic polarization-state transport from true depolarization and compares detector-side calibration, encoder pre-compensation, one shared liquid-crystal polarization retarder (LCPR), and a segmented-LCPR upper bound. Using a unified aluminum model at 4.0 um, the mirror displaces the encoded states by several degrees but does not depolarize a fully polarized monochromatic ray. Across 60 Monte Carlo trials with 2000 samples per channel, all principal methods remain approximately 100% accurate at an additive Gaussian-noise standard deviation of σ = 0.02, normalized relative to unit S₀. At σ = 0.15, the ideal-codebook, calibrated-codebook, and pre-compensated decoders achieve 90.87%, 91.24%, and 89.87%, respectively. A shared LCPR provides little global benefit, whereas segmented settings recover individual states at the cost of channel-resolved hardware. The results show that mirror-aware calibration, code-space separation, and physically implementable compensation must be considered jointly.