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.

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 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.

Microstrip X-ray detector with a very high dynamic range based on LPE-GaAs

1998 ◽  
Vol 45 (3) ◽  
pp. 724-727 ◽  
Author(s):  
A. Kaluza ◽  
T. Ohms ◽  
C. Rente ◽  
R. Engels ◽  
R. Reinartz ◽  
...  
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
X Ray ◽  
High Dynamic ◽  
Very High

scholarly journals High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content

2009 ◽  
Vol 28 (8) ◽  
pp. 2343-2367 ◽  
Author(s):  
Francesco Banterle ◽  
Kurt Debattista ◽  
Alessandro Artusi ◽  
Sumanta Pattanaik ◽  
Karol Myszkowski ◽  
...  
Keyword(s):  
Range Expansion ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
High Dynamic ◽  
Low Dynamic Range
Sign in / Sign up

Export Citation Format

Share Document