Airborne optical sectioning, an effective aerial synthetic aperture imaging technique for revealing artifacts occluded by forests, requires precise measurements of drone poses. In this letter, we present a new approach for reducing pose estimation errors beyond the possibilities of conventional Perspective-n-Point solutions by considering the underlying optimization as a focusing problem. We present an efficient image integration technique, which also reduces the parameter search space to achieve realistic processing times, and improves the quality of resulting synthetic integral images.

In this letter, we describe and validate the first fully automatic parameter optimization for thermal synthetic aperture visualization. It replaces previous manual exploration of the parameter space, which is time-consuming and error-prone. We prove that the visibility of targets in thermal integral images is proportional to the variance of the targets' image. Since this is invariant to occlusion, it represents a suitable objective function for optimization. Our findings have the potential to enable fully autonomous search and recuse operations with camera drones.

Light-field cameras offer new imaging possibilities compared to conventional digital cameras. However, the additional angular domain of light fields prohibits direct application of frequently used image processing algorithms, such as warping, retargeting, or stitching. We present a general and efficient framework for nonuniform light-field warping, which forms the basis for extending many of these image processing techniques to light fields. It propagates arbitrary spatial deformations defined in one light-field perspective consistently to all other perspectives by means of 4D patch matching instead of relying on explicit depth reconstruction. This allows processing light-field recordings of complex scenes with non-Lambertian properties such as transparency and refraction. We show application examples of our framework in panorama light-field imaging, light-field retargeting, and artistic manipulation of light fields.

Light-field cameras offer new imaging possibilities compared to conventional digital cameras. However, the additional angular domain of light fields prohibits direct application of frequently used image processing algorithms, such as warping, retargeting, or stitching. We present a general and efficient framework for nonuniform light-field warping, which forms the basis for extending many of these image processing techniques to light fields. It propagates arbitrary spatial deformations defined in one light-field perspective consistently to all other perspectives by means of 4D patch matching instead of relying on explicit depth reconstruction. This allows processing light-field recordings of complex scenes with non-Lambertian properties such as transparency and refraction. We show application examples of our framework in panorama light-field imaging, light-field retargeting, and artistic manipulation of light fields.

We present a simple guided super-resolution technique for increasing directional resolution without reliance on depth estimation or image correspondences. Rather, it searches for best-matching multidimensional (4D or 3D) patches within the entire captured data set to compose new directional images that are consistent in both the spatial and the directional domains. We describe algorithms for guided upsampling, iterative guided upsampling, and sampling code estimation. Our experimental results reveal that the outcomes of existing light-field camera arrays and lightstage systems can be improved without additional hardware requirements or recording effort simply by realignment of cameras or light sources to change their sampling patterns.

Abstract Capturing exposure sequences to compute high dynamic range (HDR) images causes motion blur in cases of camera movement. This also applies to light‐field cameras: frames rendered from multiple blurred HDR light‐field perspectives are also blurred. While the recording times of exposure sequences cannot be reduced for a single‐sensor camera, we demonstrate how this can be achieved for a camera array. Thus, we decrease capturing time and reduce motion blur for HDR light‐field video recording. Applying a spatio‐temporal exposure pattern while capturing frames with a camera array reduces the overall recording time and enables the estimation of camera movement within one light‐field video frame. By estimating depth maps and local point spread functions (PSFs) from multiple perspectives with the same exposure, regional motion deblurring can be supported. Missing exposures at various perspectives are then interpolated.