High dynamic range, sub-diffuse reflectance imaging over a wide field using an active line scan

Author(s):  
Samuel S. Streeter ◽  
Benjamin W. Maloney ◽  
Keith D. Paulsen ◽  
Brian W. Pogue
2018 ◽  
Vol 11 (4) ◽  
pp. 2041-2049 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler

Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.


2017 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler

Abstract. Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRSeg – an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR images for cloud segmentation and achieves very good results.


2020 ◽  
Vol 640 ◽  
pp. A80
Author(s):  
L. Zhang ◽  
L. G. Mi ◽  
M. Zhang ◽  
X. Liu ◽  
C. L. He

Sky curvature and non-coplanar effects, caused by low frequencies, long baselines, or small apertures in wide field-of-view instruments such as the Square Kilometre Array (SKA), significantly limit the imaging performance of an interferometric array. High dynamic range imaging essentially requires both an excellent sky model and the correction of imaging factors such as non-coplanar effects. New CLEAN deconvolution with adaptive-scale modeling already has the ability to construct significantly better narrow-band sky models. However, the application of wide-field observations based on modern arrays has not yet been jointly explored. We present a new wide-field imager that can model the sky on an adaptive-scale basis, and the sky curvature and the effects of non-coplanar observations with the w-projection method. The degradation caused by the dirty beam due to incomplete spatial frequency sampling is eliminated during sky model construction by our new method, while the w-projection mainly removes distortion of sources far from the image phase center. Applying our imager to simulated SKA data and the real observation data of the Karl G. Jansky Very Large Array (an SKA pathfinder) suggested that our imager can handle the effects of wide-field observations well and can reconstruct more accurate images. This provides a route for high dynamic range imaging of SKA wide-field observations, which is an important step forward in the development of the SKA imaging pipeline.


2020 ◽  
Vol 45 (23) ◽  
pp. 6378
Author(s):  
Samuel S. Streeter ◽  
Benjamin W. Maloney ◽  
Keith D. Paulsen ◽  
Brian W. Pogue

1990 ◽  
Vol 29 (28) ◽  
pp. 3985
Author(s):  
Duane A. Satorius ◽  
Theodore A. Kircher ◽  
Hugh B. Brown

2009 ◽  
Vol 97 (8) ◽  
pp. 1507-1521 ◽  
Author(s):  
David R. DeBoer ◽  
Russell G. Gough ◽  
John D. Bunton ◽  
Tim J. Cornwell ◽  
Ron J. Beresford ◽  
...  

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