scholarly journals A high dynamic range imaging algorithm: implementation and evaluation

2019 ◽  
Vol 22 (3) ◽  
pp. 293-307
Author(s):  
Vu Hong Son
Keyword(s):  
Exposure Time ◽  
Dynamic Range ◽  
Weighting Function ◽  
High Dynamic Range ◽  
Image Sensors ◽  
Current Image ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
Algorithm Implementation ◽  
Hdr Image

Camera specifications have become smaller and smaller, accompanied with great strides in technology and thinner product demands, which have led to some challenges and problems. One of those problems is that the image quality is reduced at the same time. The decrement of radius lens is also a cause leading to the sensor not absorbing a sufficient amount of light, resulting in captured images which include more noise. Moreover, current image sensors cannot preserve whole dynamic range in the real world. This paper proposes a Histogram Based Exposure Time Selection (HBETS) method to automatically adjust the proper exposure time of each lens for different scenes. In order to guarantee at least two valid reference values for High Dynamic Range (HDR) image processing, we adopt the proposed weighting function that restrains random distributed noise caused by micro-lens and produces a high quality HDR image. In addition, an integrated tone mapping methodology, which keeps all details in bright and dark parts when compressing the HDR image to Low Dynamic Range (LDR) image for display on monitors, is also proposed. Eventually, we implement the entire system on Adlink MXC-6300 platform that can reach 10 fps to demonstrate the feasibility of the proposed technology.  

Estimating Visual Saliency for Omnidirectional HDR Images

2021 ◽  
pp. 249-272
Author(s):  
Kenji Hara
Keyword(s):  
Coordinate System ◽  
Dynamic Range ◽  
Estimation Method ◽  
Visual Saliency ◽  
High Dynamic Range ◽  
Saliency Map ◽  
Overset Grid ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
Hdr Image

A unified decomposition-and-integration-based framework is presented herein for the visual saliency estimation of omnidirectional high dynamic range (HDR) images, which allows straightforward reuse of existing saliency estimation method for typical images with narrow field-of-view and low dynamic range (LDR). First, the proposed method decomposes a given omnidirectional HDR image into multiple partially overlapping LDR images with quasi-uniform spatial resolution and without polar singularities, both spatially and in intensity using a spherical overset grid and a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical images. Finally, the saliency map of each decomposed image is optimally integrated from the coordinate system of the overset grid and LDR back to the representation of the coordinate system and HDR of the original image. The proposed method is applied to actual omnidirectional HDR images and its effectiveness is demonstrated.

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 at the Quantum Limit with Single Photon Avalanche Diode-Based Image Sensors

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1166 ◽  
Author(s):  
Neale Dutton ◽  
Tarek Al Abbas ◽  
Istvan Gyongy ◽  
Francescopaolo Mattioli Della Rocca ◽  
Robert Henderson
Keyword(s):  
Dynamic Range ◽  
Single Photon ◽  
High Dynamic Range ◽  
Image Sensors ◽  
Quantum Limit ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
Avalanche Diode ◽  
High Dynamic

Depth Estimation for HDR Images

2012 ◽  
pp. 165-180
Author(s):  
S. Manikandan
Keyword(s):  
Dynamic Range ◽  
Depth Estimation ◽  
High Dynamic Range ◽  
Block Matching ◽  
Absolute Difference ◽  
Mapping Technique ◽  
Stereo Pair ◽  
Block Matching Algorithm ◽  
High Dynamic ◽  
Low Dynamic Range

In this chapter, depth estimation for stereo pair of High Dynamic Range (HDR) images is proposed. The proposed algorithm consists of two major techniques namely conversion of HDR images to Low Dynamic Range (LDR) images or Standard Dynamic Range (SDR) images and estimating the depth from the converted LDR / SDR stereo images. Local based tone mapping technique is used for the conversion of the HDR images to SDR images. And the depth estimation is done based on the corner features of the stereo pair images and block matching algorithm. Computationally much less expensive cost functions Mean Square Error (MSE) or Mean Absolute Difference (MAD) can be used for block matching algorithms. The proposed algorithm is explained with illustrations and results.

A Contribution Algorithm from LDRI to HDRI

2019 ◽  
Vol 34 (07) ◽  
pp. 2059025
Author(s):  
Junsong Luo ◽  
Shi Qiu ◽  
Yizhang Jiang ◽  
Keyang Cheng ◽  
Huping Ye ◽  
...  
Keyword(s):  
Response Curve ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sequences ◽  
Range Image ◽  
High Dynamic Range Image ◽  
Dynamic Image ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
First Time

High dynamic range image (HDRI) which is combined with low dynamic range image (LDRI) needs to be mapped to a low dynamic area to display. In the process of mapping, it is impossible to determine the contribution of low dynamic image sequences in the display images, so that it results in a problem that the low dynamic images cannot be accurately selected. In this paper, for the first time, a contribution algorithm from LDRI to HDRI according to the corresponding response curve of the camera is proposed.

scholarly journals Extending and Matching a High Dynamic Range Image from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3950
Author(s):  
Van Luan Tran ◽  
Huei-Yung Lin
Keyword(s):  
Image Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Performance Comparison ◽  
Range Image ◽  
High Dynamic Range Image ◽  
High Dynamic ◽  
The Difference ◽  
Imaging Algorithms ◽  
Low Dynamic Range

Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas.

scholarly journals State-of-the-art VLBI imaging: 3C345

1991 ◽  
Vol 131 ◽  
pp. 354-357
Author(s):  
Ann E. Wehrle ◽  
Stephen C. Unwin
Keyword(s):  
Thermal Noise ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Instrumental Effects ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
Very High

AbstractMost VLBI images have low dynamic range because they are limited by instrumental effects such as calibration errors and poor u, v-coverage. We outline the method used to make a new image of the bright quasar 3C345 which has very high dynamic range (peak-to-noise of 5000:1) and which is limited by the thermal noise, not instrumental errors. Both the Caltech VLBI package and the NRAO AIPS package were required to manipulate the data.

scholarly journals Inverse Tone Mapping Based upon Retina Response

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yongqing Huo ◽  
Fan Yang ◽  
Vincent Brost
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Tone Mapping ◽  
High Quality ◽  
Physiological Approach ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
Image Details ◽  
Inverse Tone Mapping ◽  
Expansion Scheme

The development of high dynamic range (HDR) display arouses the research of inverse tone mapping methods, which expand dynamic range of the low dynamic range (LDR) image to match that of HDR monitor. This paper proposed a novel physiological approach, which could avoid artifacts occurred in most existing algorithms. Inspired by the property of the human visual system (HVS), this dynamic range expansion scheme performs with a low computational complexity and a limited number of parameters and obtains high-quality HDR results. Comparisons with three recent algorithms in the literature also show that the proposed method reveals more important image details and produces less contrast loss and distortion.

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