scholarly journals A Precise Multi-Exposure Image Fusion Method Based on Low-level Features

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1597 ◽  
Author(s):  
Guanqiu Qi ◽  
Liang Chang ◽  
Yaqin Luo ◽  
Yinong Chen ◽  
Zhiqin Zhu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
A Priori ◽  
High Dynamic Range ◽  
Reference Image ◽  
Dynamic Scene ◽  
Image Sharpness ◽  
Domain Structures ◽  
Structure Decomposition ◽  
Global And Local

Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.

Multi-exposure X-ray image fusion quality evaluation based on CSF and gradient amplitude similarity

2021 ◽  
pp. 1-13
Author(s):  
Yanjie Qi ◽  
Zehui Yang ◽  
Lin Kang
Keyword(s):  
Image Fusion ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Dynamic Range ◽  
Reference Image ◽  
Subjective Perception ◽  
Steel Component ◽  
Imaging Device ◽  
X Ray ◽  
Gradient Amplitude

Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes.

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.

scholarly journals Anti-Shake HDR Imaging Using RAW Image Data

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 213 ◽  
Author(s):  
Yan Liu ◽  
Bingxue Lv ◽  
Wei Huang ◽  
Baohua Jin ◽  
Canlin Li
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sensor ◽  
Image Sequences ◽  
Reference Image ◽  
Imaging Method ◽  
Camera Motion ◽  
Range Image ◽  
Real Scene ◽  
High Dynamic

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.

scholarly journals Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 24
Author(s):  
Yan-Tsung Peng ◽  
He-Hao Liao ◽  
Ching-Fu Chen
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Gamma Correction ◽  
Virtual Image ◽  
Exposure Fusion ◽  
Multi Scale ◽  
Fusion Methods ◽  
Adaptive Gamma Correction

In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results.

High Dynamic Range Image Fusion Algorithm for Moving Targets

2017 ◽  
Vol 37 (4) ◽  
pp. 0410001
Author(s):  
都琳 Du Lin ◽  
孙华燕 Sun Huayan ◽  
王帅 Wang Shuai ◽  
高宇轩 Gao Yuxuan ◽  
齐莹莹 Qi Yingying
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Range Image ◽  
Moving Targets ◽  
High Dynamic Range Image ◽  
Fusion Algorithm ◽  
High Dynamic

scholarly journals Resolution enhancement for topography measurement of high-dynamic-range surfaces via image fusion

2018 ◽  
Vol 26 (26) ◽  
pp. 34805 ◽  
Author(s):  
Jian Wang ◽  
Rong Su ◽  
Richard Leach ◽  
Wenlong Lu ◽  
Liping Zhou ◽  
...  
Keyword(s):  
Image Fusion ◽  
Dynamic Range ◽  
Resolution Enhancement ◽  
High Dynamic Range ◽  
High Dynamic
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