• Title/Summary/Keyword: Low Contrast

Search Result 2,104, Processing Time 0.025 seconds

An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization (서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘)

  • Kim, Joung-Youn;Kim, Lee-Sup;Hwang, Seung-Ho
    • Journal of the Korean Institute of Telematics and Electronics S
    • /
    • v.36S no.12
    • /
    • pp.58-66
    • /
    • 1999
  • In this paper, an advanced histogram equalization algorithm for contrast enhancement is presented. Histogram equalization is the most popular algorithm. Global histogram equalization is simple and fast, but its contrast enhancement power is relatively low. Local histogram equalization, on the other hand, can enhance overall contrast more effectively, but the complexity of computation required is very high. In this paper, a low pass filter type mask is used to get a sub-block histogram equalization function to more simply produce the high contrast. The low pass filter type mask is realized by partially overlapped sub-block histogram equalization (POSHE). With the proposed method. the computation overhead is reduced by a factor of about one hundred compared to that of local histogram equalization while still achieving high contrast.

  • PDF

Contrast Enhancement for Segmentation of Hippocampus on Brain MR Images

  • Sengee, Nyamlkhagva;Sengee, Altansukh;Adiya, Enkhbolor;Choi, Heung-Kook
    • Journal of Korea Multimedia Society
    • /
    • v.15 no.12
    • /
    • pp.1409-1416
    • /
    • 2012
  • An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

Phonetic Contrasts of One-syllable Words and Speech Intelligibility in Adults with Hearing Impairments (청각장애 성인의 일음절 낱말대조 명료도 특성)

  • Kim Soo-Jin;Do Yeon-Ji
    • MALSORI
    • /
    • no.56
    • /
    • pp.1-13
    • /
    • 2005
  • This study examined the speech intelligibility of one-syllable words with phonetic contrasts and analyzed segmental factors that can predict the overall speech intelligibility in hearing-impaired adults. To identify the speech error characteristics, a Korean word list was audio-recorded by 7 hearing-impaired adults, and 35 listeners selected the heard word out of 5 choices. Based in part on previous studies of speech of the hearing impaired, the word list consisted of monosyllabic consonant-vowel-consonant (CVC) real word pairs. Stimulus words included 77 phonetic contrast pairs. The results showed that the percentage of errors in final position (coda) contrast was higher than in any other position in syllable. And the intelligibility deficit factors of phonetic contrast in the hearing-impaired were analyzed through stepwise regression analysis. The overall intelligibility was predicted by the error rate of manner contrast at coda, voicing contrast (homorganic triplets) at onset and high-low contrast at nucleus.

  • PDF

A Lightweight Real-Time Small IR Target Detection Algorithm to Reduce Scale-Invariant Computational Overhead (스케일 불변적인 연산량 감소를 위한 경량 실시간 소형 적외선 표적 검출 알고리즘)

  • Ban, Jong-Hee;Yoo, Joonhyuk
    • IEMEK Journal of Embedded Systems and Applications
    • /
    • v.12 no.4
    • /
    • pp.231-238
    • /
    • 2017
  • Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

Comparison of Contrast-Enhanced T2 FLAIR and 3D T1 Black-Blood Fast Spin-Echo for Detection of Leptomeningeal Metastases

  • Park, Yae Won;Ahn, Sung Jun
    • Investigative Magnetic Resonance Imaging
    • /
    • v.22 no.2
    • /
    • pp.86-93
    • /
    • 2018
  • Purpose: Imaging plays a significant role in diagnosing leptomeningeal metastases. However, the most appropriate sequence for the detection of leptomeningeal metastases has yet to be determined. This study compares the efficacies of contrast-enhanced T2 fluid attenuated inversion recovery (FLAIR) and contrast-enhanced 3D T1 black-blood fast spin echo (FSE) imaging for the detection of leptomeningeal metastases. Materials and Methods: Tube phantoms containing varying concentrations of gadobutrol solution were scanned using T2 FLAIR and 3D T1 black-blood FSE. Additionally, 30 patients with leptomeningeal metastases were retrospectively evaluated to compare conspicuous lesions and the extent of leptomeningeal metastases detected by T2 FLAIR and 3D T1 black-blood FSE. Results: The signal intensities of low-concentration gadobutrol solutions (< 0.5 mmol/L) on T2 FLAIR images were higher than in 3D T1 black-blood FSE. The T2 FLAIR sequences exhibited significantly greater visual conspicuity scores than the 3D T1 black-blood sequence in leptomeningeal metastases of the pial membrane of cistern (P = 0.014). T2 FLAIR images exhibited a greater or equal extent (96.7%) of leptomeningeal metastases than 3D T1 black-blood FSE images. Conclusion: Because of its high sensitivity even at low gadolinium concentrations, contrast-enhanced T2 FLAIR images delineated leptomeningeal metastases in a wider territory than 3D T1 black-blood FSE.

Adaptive Enhancement of Low-light Video Images Algorithm Based on Visual Perception (시각 감지 기반의 저조도 영상 이미지 적응 보상 증진 알고리즘)

  • Li Yuan;Byung-Won Min
    • Journal of Internet of Things and Convergence
    • /
    • v.10 no.2
    • /
    • pp.51-60
    • /
    • 2024
  • Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

Contrast Enhancement for Defects Extraction from Seel-tube X-ray Images (결함추출을 위한 강판튜브 엑스선 영상의 명암도 향상)

  • Hwang, Jung-Won;Hwang, Jae-Ho
    • Proceedings of the IEEK Conference
    • /
    • 2007.07a
    • /
    • pp.361-362
    • /
    • 2007
  • We propose a contrast-controlled feature detection approach for steel radiograph image. X-ray images are low contrast, dark and high noise image. So, It is not simple to detect defects directly in automated radiography inspection system. Contrast enhancement, histogram equalization and median filter are the most frequently used techniques to enhance the X-ray images. In this paper, the adaptive control method based on contrast limited histogram equalization is compared with several histogram techniques. Through comparative analysis, CLAHE(contrast controlled adaptive histogram equalization) can enhance detection of defects better.

  • PDF

Edge Detection based on Contrast Analysis in Low Light Level Environment (저조도 환경에서 명암도 분석 기반의 에지 검출)

  • Park, Hwa-Jung;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 2022.05a
    • /
    • pp.437-440
    • /
    • 2022
  • In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

  • PDF

An Image Contrast Enhancement Technique Using the Improved Integrated Adaptive Fuzzy Clustering Model (개선된 IAFC 모델을 이용한 영상 대비 향상 기법)

  • 이금분;김용수
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.11 no.9
    • /
    • pp.777-781
    • /
    • 2001
  • This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved IAFC model is used to classify the image into two classes. The proposed method is applied to several experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.

  • PDF

Contrast Enhancement Method using Color Components Analysis (컬러 성분 분석을 이용한 대비 개선 방법)

  • Park, Sang-Hyun
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.14 no.4
    • /
    • pp.707-714
    • /
    • 2019
  • Recently, as the sensor network technologies and camera technologies develops, there are increasing needs by combining two technologies to effectively observe or monitor the areas that are difficult for people to access by using the visual sensor network. Since the applications using visual sensors take pictures of the outdoor areas, the images may not be well contrasted due to cloudy weather or low-light time periods such as a sunset. In this paper, we first model the color characteristics according to illumination using the characteristics of visual sensors that continuously capture the same area. Using this model, a new method for improving low contrast images in real time is proposed. In order to make the model, the regions of interest consisting of the same color are set up and the changes of color according to the brightness of images are measured. The gamma function is used to model color characteristics using the measured data. It is shown by experimental results that the proposed method improves the contrast of an image by adjusting the color components of the low contrast image simply and accurately.