ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content

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.

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A Contribution Algorithm from LDRI to HDRI

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420590259 ◽

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.

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

Sensors ◽

10.3390/s20143950 ◽

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.

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