Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.

A new projection-based color-to-gray conversion method is proposed in this letter. In the proposed method, an objective function which considers color contrasts in an input image is defined. Projection coefficients are determined by minimizing the objective function. Experimental results show the validity of the proposed method.

In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

The ignored side effect reflecting in the introduction of mismatching brought by contrast enhancement in representative SIFT based vein recognition model is investigated. To take advantage of contrast enhancement in increasing keypoints generation, hierarchical keypoints selection and mismatching removal strategy is designed to obtain state-of-the-art recognition result.

This paper reports on the relationships between the performance of retro-reflectors and the sharpness of an aerial image formed with aerial imaging by retro-reflection (AIRR). We have measured the retro-reflector divergence angle and evaluated aerial image sharpness by use of the contrast-transfer function. It is found that the divergence angle of the retro-reflected light is strongly related to the sharpness of the aerial image formed with AIRR.

A method of color scheme is proposed considering contrast of luminance between adjacent regions and design property. This method aims at setting the contrast of luminance high, in order to make the image understandable to visually handicapped people. This method also realizes preferable color design for visually normal people by assigning color components from color combination samples. Interactive evolutionary computing is adopted to design the luminance and the color, so that the luminance and color components are assigned to each region appropriately on the basis of human subjective criteria. Here, the luminance is designed first, and then color components are assigned, keeping the luminance unchanged. Since samples of fine color combinations are applied, the obtained color design is also fine and harmonic. Computer simulations verify the high performance of this system.

There are often low contrast Mycobacterium tuberculosis (MTB) objects in the MTB images. Based on improved histogram equalization (HE), a framework of contrast enhancement is proposed to increase the contrast of MTB images. Our proposed algorithm was compared with the traditional HE and the weighted thresholded HE. The experimental results demonstrate that our proposed algorithm has better performance in contrast enhancement, artifacts suppression, and brightness preserving for MTB images.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

One of the major applications of contrast-enhanced ultrasound (CEUS) is lesion classification. After contrast agents are administered, it is possible to identify a lesion type from its enhancement pattern. However, CEUS image reading is not easy because there are various types of enhancement patterns even for the same type of lesion, and clear classification criteria have not yet been defined. Some studies have used conventional time intensity curves (TICs), which show the vessel dynamics of a lesion. It is possible to predict lesion type from the TIC parameters, such as the coefficients obtained by curve fitting, peak intensity, flow rate and time to peak. However, these parameters are not always provide sufficient accuracy. In this paper, we prepare 1D Haar-like features which describe intensity changes in a TIC and adopt the Adaboost machine learning technique, which eases understanding of which features are useful. Hyperparameters of weak classifiers, e.g., the step size of a Haar-like filter length and threshold for output of the filter, are optimized by searching for those parameters that give the best accuracy. We evaluate the proposed method using 36 focal splenic lesions in canines 16 of which were benign and 20 malignant. The accuracies were 91.7% (33/36) when inspected by an experienced veterinarian, 75.0% (27/36) by linear discriminant analysis (LDA) using conventional three TIC parameters: time to peak, area under curve and peak intensity, and 91.7% (33/36) using our proposed method. McNemar testing shows the p-value to be less than 0.05 between the proposed method and LDA. This result shows the statistical significance of differences between the proposed method and the conventional TIC analysis method using LDA.

In this paper, we propose an algorithm for contrast enhancement based on Adaptive Histogram Equalization (AHE) to improve image quality. Most histogram-based contrast enhancement methods have problems with excessive or low image contrast enhancement. This results in unnatural output images and the loss of visual information. The proposed method manipulates the slope of the input of the Probability Density Function (PDF) histogram. We also propose a pixel redistribution method using convolution to compensate for excess pixels after the slope modification procedure. Our method adaptively enhances the contrast of the input image and shows good simulation results compared with conventional methods.

In this paper, we introduce a biologically-motivated model to detect image saliency. The model employs an isophote based operator to detect potential structure and global saliency information related to each pixel, which are then combined with integral image to build up final saliency maps. We show that the proposed model outperforms seven state-of-the-art saliency detectors in experimental studies.

Display devices play image files, of which contrast enhancement methods are usually employed to bring out visual details to achieve better visual quality. However, applied to high resolution images, the contrast enhancement method entails high computation costs mostly due to histogram computations. Therefore, this letter proposes a parallel histogram calculation algorithm using the column histograms and difference histograms to reduce histogram computations. Experimental results show that the proposed algorithm is effective for histogram-based image contrast enhancement.

A specification for digital cinema systems which deal with movies digitally from production to delivery as well as projection on the screens is recommended by DCI (Digital Cinema Initiative), and the systems based on this specification have already been developed and installed in theaters. The parameters of the systems that play an important role in determining image quality include image resolution, quantization bit depth, color space, gamma characteristics, and data compression methods. This paper comparatively discusses a relation between required bit depth and gamma quantization using both of a human visual system for grayscale images and two color difference models for color images. The required bit depth obtained from a contrast sensitivity function against grayscale images monotonically decreases as the gamma value increases, while it has a minimum value when the gamma is 2.9 to 3.0 from both of the CIE 1976 L* a* b* and CIEDE2000 color difference models. It is also shown that the bit depth derived from the contrast sensitivity function is one bit greater than that derived from the color difference models at the gamma value of 2.6. Moreover, a comparison between the color differences computed with the CIE 1976 L* a* b* and CIEDE2000 leads to a same result from the view point of the required bit depth for digital cinema systems.

The optimization of polarimetric contract enhancement (OPCE) is one of the important problems in radar polarimetry since it provides a substantial benefit for target enhancement. Considering different scattering mechanisms between the desired targets and the undesired targets, Yang et al. extended the OPCE model to the generalized OPCE (GOPCE) problem. Based on a modified GOPCE model and the linear discriminant analysis, a ship detector is proposed in this paper to improve the detection performance for polarimetric Synthetic Aperture Radar (SAR) imagery. In the proposed method, we modify the combination form of the three polarimetric parameters (i.e., the plane scattering similarity parameter, the diplane scattering similarity parameter and the Cloude entropy), then use an optimization function resembling the classical Fisher criterion to optimize the optimal polarization states corresponding to the radar received power and the fusion vector corresponding to the polarimetric parameters. The principle of the optimization detailed in this paper lies in maximizing the difference between the desired targets and sea clutter, and minimizing the clutter variance at the same time. RADARSAT-2 polarimetric SAR data acquired over Tanggu Port (Tianjin, China) on June 23, 2011 are used for validation. The experimental results show that the proposed method improves the contrast of the targets and sea clutter and meanwhile reduces the clutter variance. In comparison to another GOPCE based ship detector and the classical polarimetric whitening filter (PWF), the proposed method shows a better performance for weak targets. In addition, we also use the RADARSAT-2 data acquired over San-Francisco on April 9, 2008 to further demonstrate the improvement of this method for target contrast.

In this paper, we present an algorithm to segment the liver in low-contrast CT images. As the first step of our algorithm, we define a search range for the liver boundary. Then, the EM algorithm is utilized to estimate parameters of a 'Gaussian Mixture' model that conforms to the intensity distribution of the liver. Using the statistical parameters of the intensity distribution, we introduce a new thresholding technique to classify image pixels. We assign a distance feature vectors to each pixel and segment the liver by a K-means clustering scheme. This initial boundary of the liver is conditioned by the Fourier transform. Then, a Geodesic Active Contour algorithm uses the boundaries to find the final surface. The novelty in our method is the proper selection and combination of sub-algorithms so as to find the border of an object in a low-contrast image. The number of parameters in the proposed method is low and the parameters have a low range of variations. We applied our method to 30 datasets including normal and abnormal cases of low-contrast/high-contrast images and it was extensively evaluated both quantitatively and qualitatively. Minimum of Dice similarity measures of the results is 0.89. Assessment of the results proves the potential of the proposed method for segmentation in low-contrast images.

There is no clear criterion yet for evaluating wipers based on performances of wiping raindrops and visibility in forward view. In the visibility evaluation in rainy driving, it is important to examine spatial frequency and contrast of objects in forward view. Spatial frequency and contrast of image which were affected by raindrops are calculated based on them of background board which were printed stripe patterns. Variations with time of power of analysed frequency and decreased contrast are synchronized with motion of the wiper for the all experimental cases. Moreover, we executed questionnaire, and evaluated the view of the background board. These results show that the proposed methods have been validated in evaluation with wiping performance.

We succeeded to develop a reference light source in the range of very low luminance using a double integrating sphere system, and calibrated a commercial spectrophotometer below 110-5 cd/m2 levels, which is 1/100 lower than the specified limit for measurement. And we improved measurements in the ultra low luminance range of displays using the calibrated commercial spectrophotometer and a dark sphere to suppress the influence of the surround.

Various contrast enhancement methods such as histogram equalization (HE) and local contrast enhancement (LCE) have been developed to increase the visibility and details of a degraded image. We propose an image contrast enhancement method based on the global and local adjustment of gray levels by combining HE with LCE methods. For the optimal combination of both, we introduce a discrete entropy. Evaluation of our experimental results shows that the proposed method outperforms both the HE and LCE methods.

The use of image contrast enhancement has become increasingly essential due to the need to better show the visual information contained within the image for all vision-based systems. This has lead to motivation for the design of a powerful and accurate automatic contrast enhancement for a digital image. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the automatic histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.