• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.25-33
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• 2007

In this paper, I propose a method which can enhance the visual quality of IRST images based on a loaded location in ship and an AOS. The IRST adjusts an AOS to detect targets with various altitudes because of its narrow vertical field of view and offers various functions to enhance images with its low contrast. In the proposed method, images are divided into two regions of sea and sky on the basis of the horizon after establishing relation between an AOS and a horizon location within an image. As a result, image enhancement of the proposed method is performed adaptively according to the divided region while that of conventional method is performed for entire image without the region division. Simulation results show that the proposed method represents higher visibility compared with conventional one.

• Journal of Gastric Cancer
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• v.21 no.2
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• pp.142-154
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• 2021

Purpose: Screening image-enhanced endoscopy for gastrointestinal malignant lesions has progressed. However, the influence of the color enhancement settings for the laser endoscopic system on the visibility of lesions with higher color contrast than their surrounding mucosa has not been established. Materials and Methods: Forty early gastric cancers were retrospectively evaluated using color enhancement settings C1 and C2 for laser endoscopic systems with blue laser imaging (BLI), BLI-bright, and linked color imaging (LCI). The visibilities of the malignant lesions in the stomach with the C1 and C2 color enhancements were scored by expert and non-expert endoscopists and compared, and the color differences between the malignant lesions and the surrounding mucosa were assessed. Results: Early gastric cancers mainly appeared orange-red on LCI and brown on BLI-bright or BLI. The surrounding mucosae were purple on LCI regardless of the color enhancement but brown or pale green with C1 enhancement and dark green with C2 enhancement on BLI-bright or BLI. The mean visibility scores for BLI-bright, BLI, and LCI with C2 enhancement were significantly higher than those with C1 enhancement. The superiority of the C2 enhancement was not demonstrated in the assessments by non-experts, but it was significant for experts using all modes. The C2 color enhancement produced a significantly greater color difference between the malignant lesions and the surrounding mucosa, especially with the use of BLI-bright (P=0.033) and BLI (P<0.001). C2 enhancement tended to be superior regardless of the morphological type, Helicobacter pylori status, or the extension of intestinal metaplasia around the cancer. Conclusions: Appropriate color enhancement settings improve the visibility of malignant lesions in the stomach and color contrast between the malignant lesions and the surrounding mucosa.

• Korean Journal of Radiology
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• v.21 no.11
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• pp.1210-1219
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• 2020

Objective: To compare the utility and diagnostic performance of automated breast ultrasound system (ABUS) with that of handheld ultrasound (HHUS) in evaluating pure non-mass enhancement (NME) lesions on breast magnetic resonance imaging (MRI). Materials and Methods: One hundred twenty-six consecutive MRI-visible pure NME lesions of 122 patients with breast cancer were assessed from April 2016 to March 2017. Two radiologists reviewed the preoperative breast MRI, ABUS, and HHUS images along with mammography (MG) findings. The NME correlation rate and diagnostic performance of ABUS were compared with that of HHUS, and the imaging features associated with ABUS visibility were analyzed. Results: Among 126 pure NME lesions, 100 (79.4%) were malignant and 26 (20.6%) were benign. The overall correlation rate was 87.3% (110/126) in ABUS and 92.9% (117/126) in HHUS. The sensitivity and specificity were 87% and 50% for ABUS and 92% and 42.3% for HHUS, respectively, with no significant differences (p = 0.180 and 0.727, respectively). Malignant NME was more frequently visualized than benign NME lesions on ABUS (93% vs. 65.4%, p = 0.001). Significant factors associated with the visibility of ABUS were the size of NME lesions on MRI (p < 0.001), their distribution pattern (p < 0.001), and microcalcifications on MG (p = 0.027). Conclusion: ABUS evaluation of pure NME lesions on MRI in patients with breast cancer is a useful technique with high visibility, especially in malignant lesions. The diagnostic performance of ABUS was comparable with that of conventional HHUS in evaluating NME lesions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.814-817
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• 2013

In this paper, we propose a visibility enhancement method using dehazing method in fog situation. The proposed method calculate low bound of the transmission rate that indicate fog rate and transmission that processed power operation in each pixel by the user's control. And we obtain the dehazed image using calculated transmission rate. Proposed method is possible real-time processing, because the method don't cause halo effect and drop operations from filtering by closed form. We can obtain the dehazed image in various fog conditions by user control that strength of removing fog can be adjusted according to the dgree of fog.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.36-40
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• 2017

With an attempt to enhance the visibility of laser beam, we have investigated a black matrix with scattering particles by ray tracing simulations. As the scattering particle density is increased, the detected power by the receiver is increased, thereby enhancing the visibility. In reality, the visibility is reduced with increasing incident angle (away from the normal incidence) of laser beam, a phenomenon also observed by ray tracing simulations. It is due to the fact that the mean path is increased within a highly absorptive BM layer or a smaller number of rays hit the BM area when the incident angle is high. Embedding a number of scattering particles into BM may bring in crosstalk among pixels. However, it is negligible because scattered rays inside highly absorptive BM are re-scattered due to the high scattering particle density, decreasing the power of scattered rays into the active areas.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1117-1124
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• 2018

Contrast enhancement plays an important role in various computer vision systems, since their usability can be improved with visibility enhancement of the images affected by weather and lighting conditions. This paper introduces a histogram modification algorithm that reflects the properties of original images in order to eliminate the saturation effect and washed-out of image details due to the over-enhancement. Our method modifies the original histogram so that an additive term fill histogram pits and the gamma correction suppresses histogram spikes. The parameters for the additive term and gamma correction are adjusted automatically according to statistical properties of the images. Experimental results for various low contrast and hazy images demonstrate that the proposed contrast enhancement improves visibility and reduces haze components effectively, while preserving the characteristics of original images, than the conventional methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.160-171
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• 1994

The wide dynamic range and severely attenuated contrast in mediastinal area appearing in typical chest radiographs have often caused difficulties in effective visualization and diagnosis of lung diseases. This paper proposes a new adaptive image enhancement technique which potentially solves this problem and there by improves observer performance through image processing. In the proposed method image processing is applied to the chest radiograph with different processing parameters for the lung field and mediastinum adaptively since there are much differences in anatomical and imaging properties between these two regions. To achieve this the chest radiograph is divided into the lung and mediastinum by gray level thresholding using the cumulative histogram and the dynamic range compression and local contrast enhancement are carried out selectively in the mediastinal region. Thereafter a gray scale transformation is performed considering the JND(just noticeable difference) characteristic for effective image displa. The processed images showed apparenty improved contrast in mediastinum and maintained moderate brightness in the lung field. No artifact could be observed. In the visibility evaluation experiment with 5 radiologists the processed images with better visibility was observed for the 5 important anatomical structures in the thorax.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.77-86
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• 2014

Mobile devices are generally using app images which are artificially designed. Accordingly, this paper presents adjusting device brightness based on app image categorization for enhancing the visibility under various light condition. First, the proposed method performed two prior subjective tests under various lighting conditions for selecting features of app images concerning visibility and for selecting satisfactory range of device brightness for each app image. Then, the relationship between selected features of app image and satisfactory range of device brightness is analyzed. Next, app images are categorized by using two features of average brightness of app image and distribution ratio of advanced colors that are related to satisfaction range of device brightness. Then, optimal device brightness for each category is selected by having the maximum frequency of satisfaction device brightness. Experimental results show that the categorized app images with optimal device brightness have high satisfaction ratio under various light conditions.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.808-811
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• 2022

Display under strong ambient lighting is perceived darker than it really is. Existing techniques for solving the problem in terms of software show limitations in that image enhancement techniques are applied regardless of ambient lighting or chrominance is not improved compared to luminance. Therefore, this paper proposes a visibility enhancement algorithm using deep learning to adaptively respond to ambient lighting values and an equation to restore optimal chrominance for luminance. The algorithm receives an ambient lighting value with the input image, and then applies a deep learning model and chrominance restoration equation to generate an image to minimize the difference between the degradation modeling of enhanced image and the input image. Qualitative evaluation proves that the algorithm shows excellent performance in improving visibility under strong ambient lighting through comparison of images applied with degradation modeling.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.16-22
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• 2009

In this paper, we propose an auto gain/offset which considers the visibility of human visual system (HVS) and the histogram of a target image jointly. In the proposed method, the lower and upper clipping thresholds are determined to maximize the averaged visibility of the contrast-stretched image. The target image is then contrast-stitched by the gain and offset derived from the clipping thresholds. We define the visibility as a quantity related to the spatial JND, which means the threshold below which any change of a pixel from its textured neighbors is not recognized by the HVS. Experimental results show that the contrast-stretched images by the proposed method have better global and local contrasts compared to the results by some conventional methods.