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Abstract
Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations.
Highlights
Dynamic optical imaging is able to acquire images in the motion condition either a moving camera observes a stationary scene, or a stationary camera observes a moving scene, or a moving camera observes a moving scene
The forward active imaging approach enables matching between the image motion vector and photogenic charges transfer to compensate for motion blur
The motion aberration limits the imaging performance at dynamic conditions due to the mismatching between the image field motion and the photo-induced charge transferring in the forward imaging
Summary
Dynamic optical imaging is able to acquire images in the motion condition either a moving camera observes a stationary scene, or a stationary camera observes a moving scene, or a moving camera observes a moving scene. The forward active imaging approaches aim at the matching between the photo-induced charge transfer speed and direction and the optical image motion velocity and direction to obtain high-quality images. Any mismatching of movement vectors (i.e. speed and direction) between the photo-induced charge transfer and optical image movement in dynamic processing would produce motion aberrations and the corresponding image is blurred. The forward active imaging approach enables matching between the image motion vector and photogenic charges transfer to compensate for motion blur. Forward passive imaging methods firstly introduce a high-speed motion sensor in the optical camera to measure images[18,19,20]. Forward passive imaging methods inevitably involve the mismatching (either motion speed, direction, or both) between the photo-induced charge transfer and optical image movement, which means motion aberrations appear in the dynamic imaging process
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