• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.295-299
• /
• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.279-282
• /
• 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 MEC can classify the image into two classes with unsupervised teaming rule. The proposed method is applied to some 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1996.06a
• /
• pp.175-180
• /
• 1996

In this paper, a new image segmentation method based on merging of two low contrast neighbor regions iteratively is proposed. It is suitable for very low bit rate coding. The proposed method reduces efficiently contour information and preserves subjective and objective image quality. It consists of image segmentation using 4-level hierarchical structure based on mathematical morphology and 1-level region merging structure using the contrast difference of two adjacent neighbor regions. For each segmented region of the third level, two adjacent neighbor regions having low contrast difference value in fourth level based on contrast difference value is merged iteratively. It preserves image quality and shows the noticeable reduction of the contour information, so that it can improve the bottleneck problem of segmentation-based coding at very low bit rate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.04a
• /
• pp.269-272
• /
• 2005

The principal objective of enhancement methods is to process an image so that the result is more suitable than the original image for a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compare the structure of ICECA(Image Contrast Enhancement technique using Clustering Algorithm) with the structures of HE(Histogram Equalization), BBHE(Brightness preserving Bi-Histogram Equalization), and Multi -Scale Retinex(MSR). We compared performances of image enhancement methods by applying these methods to a set of diverse images.

• ETRI Journal
• /
• v.41 no.6
• /
• pp.797-810
• /
• 2019

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high-contrast background, but they have no effect on the extraction of a salient object from images with complex low-contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics-like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low-contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low-contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state-of-the-art approaches, the proposed approach performs well.

• Korean Journal of Digital Imaging in Medicine
• /
• v.11 no.2
• /
• pp.51-57
• /
• 2009

The purpose of this study would evaluate if having clinical effects on diffusion image with quantitative analysis through ADC values of brain's normal tissue and lesions before and after contrast injections using a 3.0T. From November in 2007 until December in 2008, a total of 32 patient was performed on 3.0T(Signa Excite, GE Medical System, USA) with the normal or lesions in the patient who requests diffusion weighted image with 8channel head coil. The pulse sequence was used with spin echo EPI(TR: 10000msec, TE: 72.2 msec, Matrix: 128*128, FOV: 240 mm, NEX: 1, diffusion direction: 3, b-value: 1000). Measurement results of ADC values on lesions, CSF, white matter, gray matter, lesions after contrast injection were measured less 75% than before contrast injection, infarction: 100%, CSF: 78%(high), white matter: 71.4%(low), gray matter: 50%(high, low). The results of paired t-test on the deference of ADC values which statically is significant in three(lesions, CSF, white matter)regions except for white matter(p<0.05). Quantitative analysis of lesions, CSF, white matter, gray matter have difference on all regions. ADC values were low in lesions and white matter, normal CSF after contrast injection commonly is high than before contrast injection, ADC values which white matter were high and low (50:50) after contrast injection. 3.0T diffusion weighted image clinically supposed that performing DWI examination after contrast injection was not desirable because of having effects on brain tissue.

• Korean Journal of Remote Sensing
• /
• v.28 no.3
• /
• pp.287-296
• /
• 2012

Although low spatial resolution satellite images like MODIS and GOCI can be important to observe land surface, it is often difficult to visually interpret the imagery because of the low contrast by prevailing cloud covers. We proposed a simple and adaptive stretching algorithm to enhance image contrast over land areas in cloudy images. The proposed method is basically a linear algorithm that stretches only non-cloud pixels. The adaptive linear stretch method uses two values: the low limit (L) from image statistics and upper limit (U) from low boundary value of cloud pixels. The cloud pixel value was automatically determined by pre-developed empirical function for each spectral band. We used MODIS and GOCI images having various types of cloud distributions and coverage. The adaptive contrast stretching method was evaluated by both visual interpretation and statistical distribution of displayed brightness values.

• Journal of Korea Multimedia Society
• /
• v.11 no.4
• /
• pp.462-470
• /
• 2008

This study applies fuzzy functions to improve image quality under the assumption that uncertainty of image information due to low contrast is based on vagueness and ambiguity of the brightness pixel values. To solve the problem of low contrast images whose brightness distribution is inclined, we use the k-means algorithm as a parameter of the fuzzy function, through which automatic critical points can be found to differentiate objects from background and contrast between bright and dark points can be improved. The fuzzy function is presented at the three main stages presented to improve image quality: fuzzification, contrast enhancement and defuzzification. To measure improved image quality, we present the fuzzy index and entropy index and in comparison with those of histogram equalization technique, it shows outstanding performance.

• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.1
• /
• pp.8-16
• /
• 2012

An image obtained from a low light environment results in a low-exposure problem caused by non-ideal camera settings, i.e. aperture size and shutter speed. Of particular note, the multiple color-filter aperture (MCA) system inherently suffers from low-exposure problems and performance degradation in its image classification and registration processes due to its finite size of the apertures. In this context, this paper presents a novel method for the color enhancement of low-exposure images and its application to color shift model-based MCA system for image refocusing. Although various histogram equalization (HE) approaches have been proposed, they tend to distort the color information of the processed image due to the range limits of the histogram. The proposed color enhancement algorithm enhances the global brightness by analyzing the basic cause of the low-exposure phenomenon, and then compensates for the contrast degradation artifacts by using an adaptive histogram specification. We also apply the proposed algorithm to the preprocessing step of the refocusing technique in the MCA system to enhance the color image. The experimental results confirm that the proposed method can enhance the contrast of any low-exposure color image acquired by a conventional camera, and is suitable for commercial low-cost, high-quality imaging devices, such as consumer-grade camcorders, real-time 3D reconstruction systems, digital, and computational cameras.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.4043-4064
• /
• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.