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.

scholarly journals A New Photographic Reproduction Method Based on Feature Fusion and Virtual Combined Histogram Equalization

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6038
Author(s):  
Yu-Hsiu Lin ◽  
Kai-Lung Hua ◽  
Yung-Yao Chen ◽  
I-Ying Chen ◽  
Yun-Chen Tsai
Keyword(s):  
Visual Information ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Local Features ◽  
Compression Process ◽  
Detection Scheme ◽  
Photographic Reproduction ◽  
Reproduction Method ◽  
Global And Local ◽  
Weight Map

A desirable photographic reproduction method should have the ability to compress high-dynamic-range images to low-dynamic-range displays that faithfully preserve all visual information. However, during the compression process, most reproduction methods face challenges in striking a balance between maintaining global contrast and retaining majority of local details in a real-world scene. To address this problem, this study proposes a new photographic reproduction method that can smoothly take global and local features into account. First, a highlight/shadow region detection scheme is used to obtain prior information to generate a weight map. Second, a mutually hybrid histogram analysis is performed to extract global/local features in parallel. Third, we propose a feature fusion scheme to construct the virtual combined histogram, which is achieved by adaptively fusing global/local features through the use of Gaussian mixtures according to the weight map. Finally, the virtual combined histogram is used to formulate the pixel-wise mapping function. As both global and local features are simultaneously considered, the output image has a natural and visually pleasing appearance. The experimental results demonstrated the effectiveness of the proposed method and the superiority over other seven state-of-the-art methods.

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.

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.

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.

scholarly journals MAT: A Multimodal Attentive Translator for Image Captioning

2017 ◽  
Author(s):  
Chang Liu ◽  
Fuchun Sun ◽  
Changhu Wang ◽  
Feng Wang ◽  
Alan Yuille
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Visual Information ◽  
State Of The Art ◽  
Input Image ◽  
Target Sequence ◽  
Image Captioning ◽  
Proposed Model ◽  
Image Caption Generation ◽  
Image Caption

In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional neural network (CNN) feature, we propose to represent the input image as a sequence of detected objects which feeds as the source sequence of the RNN model. In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model. To represent the image in a sequential way, we extract the objects features in the image and arrange them in a order using convolutional neural networks. To further leverage the visual information from the encoded objects, a sequential attention layer is introduced to selectively attend to the objects that are related to generate corresponding words in the sentences. Extensive experiments are conducted to validate the proposed approach on popular benchmark dataset, i.e., MS COCO, and the proposed model surpasses the state-of-the-art methods in all metrics following the dataset splits of previous work. The proposed approach is also evaluated by the evaluation server of MS COCO captioning challenge, and achieves very competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).

scholarly journals Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
P. Jagatheeswari ◽  
S. Suresh Kumar ◽  
M. Mary Linda
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Dynamic Range ◽  
Median Filter ◽  
Histogram Equalization ◽  
Input Image ◽  
Enhancement Technique ◽  
Image Contrast Enhancement ◽  
The Mean ◽  
Plateau Limit

The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

scholarly journals Tone Reproduction Curve: rendering intents and their realization in halftone printing

2020 ◽  
Vol 11 (2) ◽  
pp. 47-59
Author(s):  
Yuri V. Kuznetsov ◽  
◽  
Andrey A. Schadenko ◽  
Vycheslav V. Vaganov ◽  
◽  
...  
Keyword(s):  
Visual Information ◽  
Dynamic Range ◽  
Input Image ◽  
Gray Scale ◽  
Printing Technology ◽  
Tone Reproduction ◽  
Image File ◽  
Reproduction Curve ◽  
Multiple Stages

Approaches to determining the Tone Reproduction Curve (TRC) which provides the reliable transfer of visual information in typical conditions of the halftone gray scale compression in relation to dynamic range of a graphic original or input image file are overviewed. The issues of such curve realization are also analyzed with taking into account the specifics of multiple stages of illustrative printing technology.

scholarly journals Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
State Of The Art ◽  
Image Database ◽  
Histogram Equalization ◽  
Input Image ◽  
Public Image ◽  
Enhancement Method ◽  
Mean And Variance ◽  
The Mean ◽  
Histogram Bins

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

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