Effect of dynamic range input image on performance of binary subtracted joint transform correlator

1996 ◽  
Vol 3 (6) ◽  
pp. 505-511 ◽  
Author(s):  
Takumi Minemoto ◽  
Yukihisa Osugi ◽  
Hiromitsu Mizukawa ◽  
Junko Ishikawa
Keyword(s):  
Dynamic Range ◽  
Input Image ◽  
Joint Transform Correlator

Effect of dynamic range input image on performance of binary subtracted joint transform correlator

1996 ◽  
Vol 3 (6) ◽  
pp. A505-A511
Author(s):  
Takumi Minemoto ◽  
Yukihisa Osugi ◽  
Hiromitsu Mizukawa ◽  
Junko Ishikawa
Keyword(s):  
Dynamic Range ◽  
Input Image ◽  
Joint Transform Correlator

scholarly journals Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

scholarly journals Detail Enhancement for Infrared Images Based on Propagated Image Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yishu Peng ◽  
Yunhui Yan ◽  
Jiuliang Zhao
Keyword(s):  
Dynamic Range ◽  
Gain Control ◽  
Bilateral Filter ◽  
Input Image ◽  
Base Layer ◽  
Infrared Images ◽  
Image Filter ◽  
Histogram Modification ◽  
Detail Enhancement ◽  
Infrared Data

For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.

scholarly journals Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4136
Author(s):  
Yung-Yao Chen ◽  
Kai-Lung Hua ◽  
Yun-Chen Tsai ◽  
Jun-Hua Wu
Keyword(s):  
Visual Information ◽  
Dynamic Range ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Input Image ◽  
Processing Stage ◽  
Global Contrast ◽  
Photographic Reproduction ◽  
Reproduction Method ◽  
Reproduction Stage

Photographic reproduction and enhancement is challenging because it requires the preservation of all the visual information during the compression of the dynamic range of the input image. This paper presents a cascaded-architecture-type reproduction method that can simultaneously enhance local details and retain the naturalness of original global contrast. In the pre-processing stage, in addition to using a multiscale detail injection scheme to enhance the local details, the Stevens effect is considered for adapting different luminance levels and normally compressing the global feature. We propose a modified histogram equalization method in the reproduction stage, where individual histogram bin widths are first adjusted according to the property of overall image content. In addition, the human visual system (HVS) is considered so that a luminance-aware threshold can be used to control the maximum permissible width of each bin. Then, the global tone is modified by performing histogram equalization on the output modified histogram. Experimental results indicate that the proposed method can outperform the five state-of-the-art methods in terms of visual comparisons and several objective image quality evaluations.

Color perception and recognition method for Guangdong embroidery image based on discrete mathematical model

2020 ◽  
pp. 1-11
Author(s):  
Ya Zhang ◽  
Qiang Xiong
Keyword(s):  
Mathematical Model ◽  
Spatial Model ◽  
Dynamic Range ◽  
Color Space ◽  
Color Perception ◽  
Histogram Equalization ◽  
Input Image ◽  
Pixel Image ◽  
Color Pixel ◽  
Discrete Mathematical Model

The traditional method of Guangdong embroidery image color perception recognition has poor stereoscopic color reduction. Therefore, this paper introduces discrete mathematical model to design a new method of Guangdong embroidery image color perception recognition. Through histogram equalization, the input image with relatively concentrated gray distribution is transformed into the histogram output image with approximately uniform distribution to enhance the dynamic range of pixel gray value. The image of Yuexiu is smoothed and filtered by median filtering method to remove the noise in the image of Yuexiu. The RGB spatial model and HSI spatial model of image color are constructed by normalizing the coordinates and color attributes of pixels. The RGB color space and HSI color space are transformed, and the image color perception recognition model is established to realize the color perception recognition of Guangdong embroidery image. The experimental results show that the pixels of each color in the color pixel image curve of the proposed method are as high as 800, the color pixel image curve distribution is the most intensive, and the color restoration is high.

scholarly journals Ghost-Free High Dynamic Range Imaging Using Histogram Separation and Edge Preserving Denoising

2014 ◽  
Vol 12 (3) ◽  
pp. 3329-3337
Author(s):  
Ramratan Ahirwal ◽  
Yogesh Singh Rajput ◽  
Dr. Yogendra Kumar Jain
Keyword(s):  
Dynamic Range ◽  
Dynamic Environment ◽  
High Dynamic Range ◽  
Input Image ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
Edge Preserving ◽  
Software Application ◽  
Single Input ◽  
High Dynamic

In this paper, we introduce a ghost-free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamicrange (HDR) images. The multiple image fusion based HDR method work only on condition that there is no movement ofcamera and object when capturing multiple, differently exposed low dynamic range (LDR) images. The proposed algorithmmakes three LDR images from a single input image to remove such an unrealistic condition. For this purpose a histogramseparation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram.An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in thestretching process. In the proposed algorithm final HDR image free from ghost artifacts in dynamic environment because itself-generates three LDR images from a single input image. Therefore, the proposed algorithm can be use in mobilephone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuiltor post-processing software application.

scholarly journals Retinal Image Enhancement using Ordering Gap Adjustment and Brightness Specification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Image Enhancement ◽  
Retinal Image ◽  
Dynamic Range ◽  
Input Image ◽  
Image Contrast ◽  
Effective Algorithm ◽  
Problem Domain ◽  
Image Brightness ◽  
Image Histogram ◽  
Matlab Program

Color retinal image enhancement plays an important role in improving an image quality suited for reliable diagnosis. For this problem domain, a simple and effective algorithm for image contrast and color balance enhancement namely Ordering Gap Adjustment and Brightness Specification (OGABS) was proposed. The OGABS algorithm first constructs a specified histogram by adjusting the gap of the input image histogram ordering by its probability density function under gap limiter and Hubbard’s dynamic range specifications. Then, the specified histograms are targets to redistribute the intensity values of the input image based on histogram matching. Finally, color balance is improved by specifying the image brightness based on Hubbard’s brightness specification. The OGABS algorithm is implemented by the MATLAB program and the performance of our algorithm has been evaluated against data from STARE and DiaretDB0 datasets. The results obtained show that our algorithm enhances the image contrast and creates a good color balance in a pleasing natural appearance with a standard color of lesions.

scholarly journals Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion

2020 ◽  
Vol 10 (18) ◽  
pp. 6262
Author(s):  
Feiran Chen ◽  
Jianlin Zhang ◽  
Jingju Cai ◽  
Tao Xu ◽  
Gang Lu ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Infrared Image ◽  
High Dynamic Range ◽  
Input Image ◽  
Base Layer ◽  
Dynamic Range Compression ◽  
Novel Approach ◽  
Adaptive Enhancement ◽  
High Dynamic ◽  
Detail Enhancement

The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.

Color perception and recognition method for Guangdong embroidery image based on discrete mathematical model

2021 ◽  
pp. 1063293X2199436
Author(s):  
Ya Zhang ◽  
Qiang Xiong
Keyword(s):  
Mathematical Model ◽  
Spatial Model ◽  
Traditional Method ◽  
Dynamic Range ◽  
Color Space ◽  
Color Perception ◽  
Histogram Equalization ◽  
Input Image ◽  
Recognition Method ◽  
Discrete Mathematical Model

Aiming at the problem that the traditional color perception and recognition method for Guangdong embroidery image has poor color stereo restoring ability, a color perception, and recognition method for Guangdong embroidery image based on discrete mathematical model is proposed. Through histogram equalization, the input image with centralized gray distribution is transformed into the output image with approximate uniform distribution to enhance the dynamic range of the gray value of the pixels; the median filtering method is used to smooth the Guangdong embroidery image and remove the noise in the Guangdong embroidery image. The RGB spatial model and HSI spatial model of image color are constructed by normalizing the coordinates and color attributes of pixels. Using these two models to transform RGB color space and HSI color space, image color perception, and recognition model is established to realize color perception and recognition of Guangdong embroidery image. In order to verify the color stereo restoring ability of the method, the method is compared with the traditional method for color perception and recognition of Guangdong embroidery image, which proves that the color stereo restoring ability of the method is better than that of the traditional method.

Method of features construction for remote sensing images based on the characteristics of random point fields

2017 ◽  
Vol 2017 (45) ◽  
pp. 90-95
Author(s):  
R.Ya. Kosarevych ◽  
◽  
O.A. Lutsyk ◽  
B.P. Rusyn ◽  
V.V. Korniy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dynamic Range ◽  
Texture Features ◽  
Input Image ◽  
Image Resolution ◽  
Random Point ◽  
Local Maxima ◽  
Image Fragment ◽  
Point Field ◽  
Field Element

Texture features are widely used in remote sensing image classification. In most cases they are extracted from grayscale images without taking color information into consideration. The texture descriptors, which consist of characteristics of random point fields formed for pixels of distinct intensity of grayscale and color band images are presented. The input image is divided into fragments for the elements of each of which the histogram is constructed and their local maxima are determined. Size of fragments are chosen depending on image resolution. For each of the intensity of the dynamic range of the image, a random point field, as a set of geometric centers of fragments, is formed. By the formed configuration, each field is classified as cluster, regular or random. To form a description of image elements a distribution of the number of field elements for each intensity and fragment is constructed. Separately, the vectors of the point field element for each intensity in the image fragment and the point field element for the selected intensity are formed. Experimental results demonstrate that proposed descriptors yield performance compared to other state-of-the-art texture features.

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