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.

scholarly journals A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2053
Author(s):  
Jiayu Wang ◽  
Hongquan Wang ◽  
Xinshan Zhu ◽  
Pengwei Zhou
Keyword(s):  
Discrete Cosine Transform ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Cosine Transform ◽  
Spatial Image ◽  
Multi Scale ◽  
Dct Domain ◽  
High Dynamic ◽  
Fully Connected

Although high dynamic range (HDR) is now a common format of digital images, limited work has been done for HDR source forensics. This paper presents a method based on a convolutional neural network (CNN) to detect the source of HDR images, which is built in the discrete cosine transform (DCT) domain. Specifically, the input spatial image is converted into DCT domain with discrete cosine transform. Then, an adaptive multi-scale convolutional (AMSC) layer extracts features related to HDR source forensics from different scales. The features extracted by AMSC are further processed by two convolutional layers with pooling and batch normalization operations. Finally, classification is conducted by a fully connected layer with Softmax function. Experimental results indicate that the proposed DCT-CNN outperforms the state-of-the-art schemes, especially in accuracy, robustness, and adaptability.

scholarly journals Image Enhancement Using Modified Histogram and Log-Exp Transformation

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1062 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Root Mean Square ◽  
High Intensity ◽  
Dynamic Range ◽  
State Of The Art ◽  
Mean Square ◽  
Gamma Correction ◽  
Uniform Illumination ◽  
Low Intensity ◽  
Average Contrast ◽  
Adaptive Gamma Correction

An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.

scholarly journals High-dynamic-range imaging for cloud segmentation

2018 ◽  
Vol 11 (4) ◽  
pp. 2041-2049 ◽  
Author(s):  
Soumyabrata Dev ◽  
Florian M. Savoy ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Wide Field ◽  
Single Shot ◽  
Generation Process ◽  
Image Generation ◽  
Large Dynamic Range ◽  
Exposure Fusion ◽  
Wide Field Of View ◽  
High Dynamic

Abstract. Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.

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 An Analog Gamma Correction Scheme for High Dynamic Range CMOS Logarithmic Image Sensors

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 24132-24145 ◽  
Author(s):  
Yuan Cao ◽  
Xiaofang Pan ◽  
Xiaojin Zhao ◽  
Huisi Wu
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Sensors ◽  
Gamma Correction ◽  
Correction Scheme ◽  
High Dynamic
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