In this paper, an adaptive image contrast enhancement algorithm based on an optimization problem in two dimensional Histogram domain is presented. To reduce the unwanted effects of the Histogram adjustment, through this optimization-similar to the other methods- the 2D Histogram of enhanced image is found in close proximity to input image Histogram and uniform distribution, simultaneously. In addition, different from the other methods, by adaptive adjusting the components of a weight matrix, local information is counted. Experimental results in the quantitative and qualitative assessments on a wide range of images demonstrate the performance of the proposed method. Tests have shown that with the addition of the adaptive adjusting the weights, the average performance in contrast enhancement increases 75 and 3 percent from the viewpoint of the AMBE_N and DE_N, respectively.

The Equalization of Histograms is a simple and efficient process for contrast enhancement. This paper presents Equalization of the bi-Histogram with the level of the entropy-based plateau. In the first step, the input Histogram is divided into two separate sub-Histograms, using the mean brightness as a primary threshold of total image pixels. The mentioned threshold is updated in a way that it minimizes the different values of discrete entropy between each section. Then, based on the measured plateau value, these sub-Histograms are clipped to prevent unnecessary enhancement. Finally, the image intensity is stretched based on the cumulative distribution function. Laboratory results show that this method gives better outcomes of enhancement, especially in the presence of noise, compared to some two-section methods of preserving of brightness of the Histogram Equalization.

Purpose: Breast cancer is one of the most prevalent diseases among women worldwide. One of the effective ways to reduce the risk of death from breast cancer is early detection by breast screening methods such as thermography. Thermography is non-invasive infrared imaging that detects early symptoms of breast angiogenesis based on the temperature difference and asymmetric patterns between left and right breasts. For better visual perception, it is essential to increase the medical image quality and contrast. Materials and Methods: Histogram Equalization (HE) is a common and effective technique for contrast enhancement that uses the whole dynamic range of gray levels. In this paper, we propose to apply the Equalization technique to the object part of the image rather than the background. One way is to use Otsu's method for automatic image thresholding. A more efficient approach to extract the body region is to fit a bimodal Gaussian distribution on the temperature information and restrict the Equalization on gray level ranges corresponding to temperatures between the mean minus/plus three times of standard deviation. Results: We compared the performance of the proposed approach with six conventional HE methods by using objective criteria, including Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSI), and Entropy. Conclusion: Based on objective measures, as well as subjective visual inspection of the results, the proposed Gaussian model-based HE has better performance in contrast enhancement and brightness preservation among other methods.

Several Histogram Equalization methods for enhancing the color images of Rosa Damascena flowers and some thresholding methods for segmentation of the flowers were examined. Images were taken outdoors at different times of day and light conditions. A factorial experiment in the form of a Completely Randomized Design with two factors of Histogram Equalization method at 8 levels and thresholding method at 15 levels, was implemented. Histogram Equalization methods included: CHE, BBHE, BHEPL-D, DQHEPL, DSIHE, RMSHE, RSIHE, and no Histogram Equalization (NHE) as the control. Thresholding method levels were: Huang, Intermodes, Isodata, Li, maximum entropy, mean, minimum, moments, Otsu, percentile, Renyi‟ s entropy, Shanbhag, Yen, constant, and global basic thresholding method. The effect of these factors on the properties of the segmented images such as the Percentage of Incorrectly Segmented Area (PISA), Percentage of Overlapping Area (POA), Percentage of Undetected Area (PUA), and Percentage of Detected Flowers (PDF) was investigated. Results of Histogram Equalization analysis showed that DQHEPL and NHE have the statistically significant lowest PUA (11. 13% and 8. 32%, respectively), highest POA (89. 35% and 92. 07%, respectively), and highest PDF (61. 88% and 64. 94%, respectively). Thresholding methods had a significant effect on PISA, PUA, POA, and PDF. The highest PDF belonged to constant, minimum, and Intermodes (75. 07%, 73. 08% and 74. 30%, respectively) They also had the lowest PISA (0. 35%, 1. 29%, and 1. 85%, respectively) and PUA (33. 72%, 23. 09%, and 15. 56%, respectively). These methods had the highest POA (80. 73%, 76. 70%, and 84. 67%, respectively). Hence, they are suitable methods for segmentation of Rosa Damascena flowers in color images.

Introduction: Imaging methods are progressing in a rapidly manner, but the problem which we, as the health providers always encounter with is the expensive costs of different devices and our limited budget to provide them.Aims: The aim of this study is to evaluate the usefulness of Histogram Equalization (HE) and Unsharp Mask (UM) on the conventional CXR images.Methods and Material: In Urmia University of Medical Sciences, we designed a windows-based computer program that contains Histogram Equalization (HE), unsharp mask (UM) and com-bination of HE and UM algorithms with adjusted parameters to process conventional chest x-ray (CXR) images. Two series of CXR images including 49 images without major pulmonary disorder and 45 images with pulmonary parenchymal disorders were selected. After convert-ing them to digital format, images were processed with HE, UM and combination of HE and UM techniques. In each series, original and processed images were saved in 4 databases. Two board-certified general radiologists (with 6 and 5 years experience) analyzed images. Saved images were displayed to radiologists randomly and separately. Quality of each image was saved as a scale from 1 (very low quality) to 5 (excellent). We used a variance-based statistical technique to analyze quality.Statistical analysis used: To compare the quality of each algorithm (GHE, UM and combina-tion of GHE and UM), a variance-based statistical analysis was done.Results: In the first series images, HE and combination of HE and UM algorithms increased quality of images, but UM technique was not suitable, solely. Also, all three techniques in-creased quality of second series images.Conclusions: The use of digital image processing algorithms such as HE or UM on conven-tional CXR images can increase quality of images.

Increasing contrast is one of the vital issues in meteorological image processing, which is crucial for improving image quality and weather condition detection. One of the most common methods for enhancing contrast in digital images is Histogram Equalization. This method is simple and effective, but it often leads to illogical contrast enhancement and displaying images in an unnatural and poor manner. Furthermore, the average brightness of the images is not properly preserved. In this article, a new method for balancing images while maintaining brightness is introduced. This method modifies the Histogram of the original image using fuzzy logic and controls the balancing rate by applying a thresholding process. Initially, the average intensity of gray levels is found, and the Histogram is divided into two parts. Then, by finding the average of the two sub-Histograms, the Histogram is divided into four parts. For dynamic Equalization, a new range is defined, and each of the sub-Histograms is balanced individually. Finally, a normalization process is applied to the output image to preserve the average brightness. Simulation results show that this new method has significantly improved the quality of black and white meteorological images while maintaining brightness.

In this paper, a method is proposed to automatically select reference image in Histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on Histogram of the reference image. In the conventional Histogram matching methods, user should perform several experiments on various images to find a suitable reference image. This paper presents a new method to automatically select the reference image. In this method, images are converted from RGB to HSV, and the illumination (V) components are considered to select the reference image. The appropriate reference image is selected using a similarity measure via measuring the similarity between the Histograms of the initial image and Histograms of the images in the data base. Indeed, an image with similar Histogram to the Histogram of the original images is more appropriate to choose as the reference image for Histogram matching. Results in this research indicate superiority of the proposed approach, compared to other existing approaches, in image enhancement via Histogram matching. In addition, the user would have no concern in selecting an appropriate reference image for Histogram matching in the proposed approach. This approach is applicable to both RGB and gray scale images.