A detail enhancement and dynamic range adjustment algorithm for high dynamic range images

2014 ◽  
Author(s):  
Bo Xu ◽  
Huachuang Wang ◽  
Mingtao Liang ◽  
Cong Yu ◽  
Jinlong Hu ◽  
...  
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Images ◽  
High Dynamic ◽  
Detail Enhancement

Creation Method of High Dynamic Range Image Based on Three-Color Camera

2015 ◽  
Vol 731 ◽  
pp. 193-196
Author(s):  
Jie Li ◽  
Hai Wen Wang ◽  
Xi Xi He
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Image ◽  
Range Images ◽  
Visual Evaluation ◽  
Display Devices ◽  
High Dynamic ◽  
Low Dynamic Range ◽  
Detail Enhancement ◽  
Fine Tune

The current HDR (High-Dynamic Range) images gets expensive display devices with low dynamic range of equipment problems, research objectives are presented methods for using ordinary camera fetching and displaying high dynamic range images. General three-color camera’s use is to obtain 3 different exposures of the same scene images, and binary image pyramid, followed by low-level image panning and rotation registration step by step, using HDR Darkroom Photomatix software obtains high dynamic range images ,tone mapping and detail enhancement, using Photoshop software to fine-tune to get the final high-dynamic range images. Visual evaluation and instrumental measurements shows the synthesis of high dynamic range images can increase reflects the brightness of the scene, details and colour information, application and promotion of the value of the method.

Detail Enhancement for High Dynamic Range Images Rendering Based on Multi-Level Decomposition

2014 ◽  
Vol 543-547 ◽  
pp. 2600-2604
Author(s):  
Hua Wang ◽  
Hui Zhang ◽  
Jian Zhong Cao ◽  
Zuo Feng Zhou ◽  
Lei Yang ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Bilateral Filter ◽  
High Dynamic Range ◽  
Computational Photography ◽  
Base Layer ◽  
Range Images ◽  
Tone Reproduction ◽  
High Dynamic ◽  
Multi Level ◽  
Detail Enhancement

Various tone reproduction operators have been proposed to display high dynamic range images on low dynamic range (LDR) devices. Many recent computational photography techniques decompose an image into a piecewise smooth base layer, containing large scale variations in intensity, and a residual detail layer capturing the smaller scale details in the image. In these techniques, its important to control the scale of the extracted details and it is often needed to manipulate details in order to avoid the appearance of visual artifacts. In this paper, a new method is proposed to preserve details for high dynamic range images tone reproduction using multi-level image decomposition. We show that current base-detail decomposition techniques, based on the bilateral filter, are limited in their ability to extract detail at arbitrary scales. Thus, we achieve detail enhancement by applying the bilateral filter iteratively, and this process is called multi-level decomposition. By minimizing the proposed energy function, we can choose the proper decomposition level. Simulation results demonstrate that the proposed method can acquire better visual quality in detail enhancement while make the base smoothness.

High Dynamic Range Images and Tone Mapping Operator

2009 ◽  
Vol 35 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Ke-Hu YANG ◽  
Jing JI ◽  
Jian-Jun GUO ◽  
Wen-Sheng YU
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Tone Mapping ◽  
Range Images ◽  
High Dynamic

scholarly journals Learning to display high dynamic range images

2007 ◽  
Vol 40 (10) ◽  
pp. 2641-2655 ◽  
Author(s):  
Guoping Qiu ◽  
Jiang Duan ◽  
Graham D. Finlayson
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Images ◽  
High Dynamic

scholarly journals No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features

2018 ◽  
Vol 8 (9) ◽  
pp. 1688 ◽  
Author(s):  
Jinseong Jang ◽  
Hanbyol Jang ◽  
Taejoon Eo ◽  
Kihun Bang ◽  
Dosik Hwang
Keyword(s):  
Quality Assessment ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Assessment Methods ◽  
Reference Image ◽  
Range Images ◽  
Automatic Assessment ◽  
Visual Preference ◽  
Subjective Evaluations ◽  
High Dynamic

Image adjustment methods are one of the most widely used post-processing techniques for enhancing image quality and improving the visual preference of the human visual system (HVS). However, the assessment of the adjusted images has been mainly dependent on subjective evaluations. Also, most recently developed automatic assessment methods have mainly focused on evaluating distorted images degraded by compression or noise. The effects of the colorfulness, contrast, and sharpness adjustments on images have been overlooked. In this study, we propose a fully automatic assessment method that evaluates colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images while considering HVS preferences. The proposed method does not require a reference image and automatically calculates quantitative scores, visual preference, and quality assessment with respect to the level of colorfulness, contrast, and sharpness adjustment. The proposed method evaluates adjusted images based on the features extracted from high dynamic range images, which have higher colorfulness, contrast, and sharpness than that of low dynamic range images. Through experimentation, we demonstrate that our proposed method achieves a higher correlation with subjective evaluations than that of conventional assessment methods.

Sign in / Sign up

Export Citation Format

Share Document