Abstract
AbstractWe introduce a lensless imaging framework for contemporary computer vision applications in long-wavelength infrared (LWIR). The framework consists of two parts: a novel lensless imaging method that utilizes the idea of local directional focusing for optimal binary sparse coding, and lensless imaging simulator based on Fresnel-Kirchhoff diffraction approximation. Our lensless imaging approach, besides being computationally efficient, is calibration-free and allows for wide FOV imaging. We employ our lensless imaging simulation software for optimizing reconstruction parameters and for synthetic image generation for CNN training. We demonstrate the advantages of our framework on a dual-camera system (RGB-LWIR lensless), where we perform CNN-based human detection using the fused RGB-LWIR data.KeywordsLensless imagingLong-wave infrared (LWIR) imagingDiffractive opticsImage reconstructionDiffraction simulationPedestrian detectionHuman detectionVisible-infrared image fusionFaster R-CNNs
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