Sequential Homography-Based Alignment for HDR Image Generation

2012 ◽  
Vol 452-453 ◽  
pp. 1025-1029
Author(s):  
Luigi Barazzetti ◽  
Fabio Remondino ◽  
Marco Scaioni
Keyword(s):  
Dynamic Range ◽  
Digital Camera ◽  
High Dynamic Range ◽  
The Other ◽  
Image Generation ◽  
Registration Algorithm ◽  
Other Hand ◽  
Standard Solution ◽  
High Dynamic ◽  
Hdr Image

Tripoding the camera is a standard solution to acquire aligned images useful for High Dynamic Range photography. On the other hand, the chance to use a hand-held digital camera is surely more practical and attractive for photographers. In this paper we propose a registration algorithm that recovers the alignment of several bracketed images using a progressive combination of homographies estimated from a set of image correspondences.

scholarly journals Relative sky radiance from multi-exposure all-sky camera images

2021 ◽  
Vol 14 (3) ◽  
pp. 2201-2217
Author(s):  
Juan C. Antuña-Sánchez ◽  
Roberto Román ◽  
Victoria E. Cachorro ◽  
Carlos Toledano ◽  
César López ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Quality Criteria ◽  
High Dynamic Range ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
High Dynamic ◽  
Sky Radiance ◽  
Black Level ◽  
Hdr Image

Abstract. All-sky cameras are frequently used to detect cloud cover; however, this work explores the use of these instruments for the more complex purpose of extracting relative sky radiances. An all-sky camera (SONA202-NF model) with three colour filters narrower than usual for this kind of cameras is configured to capture raw images at seven exposure times. A detailed camera characterization of the black level, readout noise, hot pixels and linear response is carried out. A methodology is proposed to obtain a linear high dynamic range (HDR) image and its uncertainty, which represents the relative sky radiance (in arbitrary units) maps at three effective wavelengths. The relative sky radiances are extracted from these maps and normalized by dividing every radiance of one channel by the sum of all radiances at this channel. Then, the normalized radiances are compared with the sky radiance measured at different sky points by a sun and sky photometer belonging to the Aerosol Robotic Network (AERONET). The camera radiances correlate with photometer ones except for scattering angles below 10∘, which is probably due to some light reflections on the fisheye lens and camera dome. Camera and photometer wavelengths are not coincident; hence, camera radiances are also compared with sky radiances simulated by a radiative transfer model at the same camera effective wavelengths. This comparison reveals an uncertainty on the normalized camera radiances of about 3.3 %, 4.3 % and 5.3 % for 467, 536 and 605 nm, respectively, if specific quality criteria are applied.

scholarly journals High-dynamic-range imaging for cloud segmentation

2018 ◽  
Vol 11 (4) ◽  
pp. 2041-2049 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Wide Field ◽  
Single Shot ◽  
Generation Process ◽  
Image Generation ◽  
Large Dynamic Range ◽  
Exposure Fusion ◽  
Wide Field Of View ◽  
High Dynamic

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.

High Dynamic Range (HDR) Image Quality Assessment: A Survey

2019 ◽  
Author(s):  
Irwan Prasetya Gunawan ◽  
Ocarina Cloramidina ◽  
Salmaa Badriatu Syafa'ah ◽  
Guson Prasamuarso Kuntarto ◽  
Berkah I Santoso
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic ◽  
Hdr Image

High-Dynamic-Range Image Generation and Coding for Multi-exposure Multi-view Images

2016 ◽  
Vol 36 (7) ◽  
pp. 2786-2814 ◽  
Author(s):  
Jui-Chiu Chiang ◽  
Po-Han Kao ◽  
Yao-Sheng Chen ◽  
Wei-Ren Chen
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Image ◽  
Image Generation ◽  
High Dynamic Range Image ◽  
High Dynamic

scholarly journals Relative sky radiance from multi-exposure all-sky camera images

2020 ◽  
Author(s):  
Juan C. Antuña-Sánchez ◽  
Roberto Román ◽  
Victoria E. Cachorro ◽  
Carlos Toledano ◽  
César López ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Quality Criteria ◽  
High Dynamic Range ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
High Dynamic ◽  
Sky Radiance ◽  
Black Level ◽  
Hdr Image

Abstract. All-sky cameras are frequently used to detect cloud cover; however, this work explores the use of these instruments for the more complex purpose of extracting relative sky radiances. An all-sky camera (SONA202-NF model) with three colour filters, narrower than usual for this kind of cameras, is configured to capture raw images at seven exposure times. A detailed camera characterization of the black level, readout noise, hot pixels and linear response is carried out. A methodology is proposed to obtain a linear high dynamic range (HDR) image and its uncertainty, which represents the relative sky radiance map at three effective wavelengths. The relative sky radiance (normalized by the sum of all radiances) is extracted from these maps and compared with the sky radiance measured at different sky points by a sun/sky photometer belonging to the Aerosol Robotic Network (AERONET). The camera radiances are in line with photometer ones excepting for scattering angles below 10º, which is probably due to some light reflections on the fisheye lens and camera dome. Camera and photometer wavelengths are not coincident, hence camera radiances are also compared with sky radiances simulated by a radiative transfer model at the same camera effective wavelengths. This comparison reveals an uncertainty on the normalized camera radiances about 3.3 %, 4.3 % and 5.3 % for 467, 536 and 605 nm, respectively, if specific quality criteria are applied.

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