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

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.

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Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization

Sensors ◽

10.3390/s21124136 ◽

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.

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An Optimal Tone Reproduction Curve Operator for the Display of High Dynamic Range Images

2005 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.2005.1466075 ◽

2005 ◽

Author(s):

Guoping Qiu ◽

Jiang Duan

Keyword(s):

Dynamic Range ◽

High Dynamic Range ◽

Range Images ◽

Tone Reproduction ◽

Reproduction Curve ◽

High Dynamic

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Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

Computational Intelligence and Neuroscience ◽

10.1155/2018/3837275 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 6

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.

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

Optical Review ◽

10.1007/bf02932059 ◽

1996 ◽

Vol 3 (6) ◽

pp. 505-511 ◽

Cited By ~ 5

Author(s):

Takumi Minemoto ◽

Yukihisa Osugi ◽

Hiromitsu Mizukawa ◽

Junko Ishikawa

Keyword(s):

Dynamic Range ◽

Input Image ◽

Joint Transform Correlator

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Ultrasound Physics and Instrumentation for Pathologists

Archives of Pathology & Laboratory Medicine ◽

10.5858/2009-0730-ra.1 ◽

2010 ◽

Vol 134 (10) ◽

pp. 1541-1556 ◽

Cited By ~ 8

Author(s):

David Lieu

Keyword(s):

Fine Needle Aspiration ◽

New Technologies ◽

Dynamic Range ◽

Needle Aspiration ◽

Physical Principle ◽

Lateral Resolution ◽

Ultrasound Guided ◽

Gray Scale ◽

Contrast Resolution ◽

Fine Needle

Abstract Context.—Interest in pathologist-performed ultrasound-guided fine-needle aspiration is increasing. Educational courses discuss clinical ultrasound and biopsy techniques but not ultrasound physics and instrumentation. Objective.—To review modern ultrasound physics and instrumentation to help pathologists understand the basis of modern ultrasound. Data Sources.—A review of recent literature and textbooks was performed. Conclusions.—Ultrasound physics and instrumentation are the foundations of clinical ultrasound. The key physical principle is the piezoelectric effect. When stimulated by an electric current, certain crystals vibrate and produce ultrasound. A hand-held transducer converts electricity into ultrasound, transmits it into tissue, and listens for reflected ultrasound to return. The returning echoes are converted into electrical signals and used to create a 2-dimensional gray-scale image. Scanning at a high frequency improves axial resolution but has low tissue penetration. Electronic focusing moves the long-axis focus to depth of the object of interest and improves lateral resolution. The short-axis focus in 1-dimensional transducers is fixed, which results in poor elevational resolution away from the focal zone. Using multiple foci improves lateral resolution but degrades temporal resolution. The sonographer can adjust the dynamic range to change contrast and bring out subtle masses. Contrast resolution is limited by processing speed, monitor resolution, and gray-scale perception of the human eye. Ultrasound is an evolving field. New technologies include miniaturization, spatial compound imaging, tissue harmonics, and multidimensional transducers. Clinical cytopathologists who understand ultrasound physics, instrumentation, and clinical ultrasound are ready for the challenges of cytopathologist-performed ultrasound-guided fine-needle aspiration and core-needle biopsy in the 21st century.

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Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽

10.3390/diagnostics9020038 ◽

2019 ◽

Vol 9 (2) ◽

pp. 38 ◽

Cited By ~ 10

Author(s):

Incheol Kim ◽

Sivaramakrishnan Rajaraman ◽

Sameer Antani

Keyword(s):

Neural Network ◽

Information Retrieval ◽

Convolutional Neural Network ◽

Visual Information ◽

Medical Image ◽

Input Image ◽

Visual Interpretation ◽

Feature Maps ◽

Novel Method ◽

Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

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