scholarly journals A Study of Unsharp Masking on HDR Visualization on low Dynamic Range Devices

2020 ◽  
Vol 2020 (28) ◽  
pp. 351-355
Author(s):  
Jakkarin Singnoo
Keyword(s):  
Dynamic Range ◽  
Image Sharpening ◽  
Alternative Framework ◽  
Unsharp Masking ◽  
Post Process ◽  
Additional Image ◽  
Low Dynamic Range

To visualize HDR contents on low dynamic range displays, a fast and efficient TMO is often preferred. One way to achieve this is to use a Global TMO. However, Global TMO often results in poor contrast tone-mapped images and often needs a postprocess that enhances the contrast, such as Unsharp Masking. This work illustrates that such Unsharp Masking can be directly integrated into several global TMOs, resulting in an alternative framework to apply the Unsharp Masking to the HDR visualization pipeline. The proposed framework is fast and delivers images with a proper contrast without the need for the additional image sharpening at the post process.

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 Blind Tone-Mapped Image Quality Assessment Based on Regional Sparse Response and Aesthetics

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 850
Author(s):  
Zhouyan He ◽  
Mei Yu ◽  
Fen Chen ◽  
Zongju Peng ◽  
Haiyong Xu ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Assessment Method ◽  
Quality Assessment Method ◽  
Scene Information ◽  
Detail Loss ◽  
Low Dynamic Range

High dynamic range (HDR) images give a strong disposition to capture all parts of natural scene information due to their wider brightness range than traditional low dynamic range (LDR) images. However, to visualize HDR images on common LDR displays, tone mapping operations (TMOs) are extra required, which inevitably lead to visual quality degradation, especially in the bright and dark regions. To evaluate the performance of different TMOs accurately, this paper proposes a blind tone-mapped image quality assessment method based on regional sparse response and aesthetics (RSRA-BTMI) by considering the influences of detail information and color on the human visual system. Specifically, for the detail loss in a tone-mapped image (TMI), multi-dictionaries are first designed for different brightness regions and whole TMI. Then regional sparse atoms aggregated by local entropy and global reconstruction residuals are presented to characterize the regional and global detail distortion in TMI, respectively. Besides, a few efficient aesthetic features are extracted to measure the color unnaturalness of TMI. Finally, all extracted features are linked with relevant subjective scores to conduct quality regression via random forest. Experimental results on the ESPL-LIVE HDR database demonstrate that the proposed RSRA-BTMI method is superior to the existing state-of-the-art blind TMI quality assessment methods.

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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