This paper presents a new automatic Image enhancement method by modifying the gamma value of its individual pixels. Most of existing gamma correction methods apply a uniform gamma value across the Image. Considering the fact that gamma variation for a single Image is actually nonlinear, the proposed method locally estimates the gamma values in an Image using support vector machine. First, a database of training Images are constructed from various standard Images under different gamma conditions. Then by windowing each of the training Images, a number of features that characterize Images content are computed from its pixel intensity histogram, gray level co-occurrence matrix, and discrete cosine transform domain. To improve the gamma values of an Image the aforementioned features are initially computed in sliding windows, then SVM is employed to estimate the gamma value in each window. In this study, it is shown that the proposed method has performed well in improving the quality of Images. Subjective and objective Image quality assessments used in this study attest superiority of the proposed method compared to the existing methods in Image quality enhancement using Image gamma value.

This paper proposes a new automatic Image enhancement method via improving the Image dynamic range. The improvement is performed via modifying the Gamma value of pixels in the Image. Gamma distortion in an Image is due to the technical limitations in the imaging device, and imposes a nonlinear effect. The severity of distortion in an Image varies depending on the texture and depth of the objects. The proposed method locally estimates the Gamma values in an Image. In this method, the Image is initially segmented using a pixon-based approach. All of the pixels in each segment have similar characteristics in terms of the need for Gamma correction. Then, the Gamma value for each segment is estimated by minimizing the homogeneity of co-occurrence matrix. This feature can represent Image details. The minimum value of this feature in a segment shows maximum details of the segment. The quality of an Image is improved once more details are presented in the Image via Gamma correction. In this study, it is shown that the proposed method performs well in improving the quality of Images. Subjective and objective Image quality assessments performed in this study attest the superiority of the proposed method compared to the existing methods in Image quality enhancement.

In this paper, a new approach is presented for improving Image quality. It provides a new outlook on how to apply the enhancment methods on Images. Image enhancement techniques may deal with the illumination, resolution, or distribution of pixels values. Issues such as the illumination of the scene and reflectance of objects affect on Image captures. Generally, the pixels value of an Image is proportional to the illumination of point in the scene and the reflectance of the object. Indeed, the captured Image is the results of illumination and reflectance of the object. Hence, impairment of the Image may be due to each of the illumination or reflectance component. In this paper, it is shown that various types of impairments have different effects on the illumination and reflectance of Image components. Studies showed that effects of Image impairment on one of its components are more than on the other component depending on the type of impairment. Unlike conventional methods which do enhancement process on the original Image for any type of impairment, in this paper it is to reduce the impairement effects from Image components. Results of this research show that Image enhancement based on the proposed method has better results compared to applying enhancement methods on original Image.

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.

Recently, many studies have examined filters for reducing or removing speckle noise, which is inherent to different Images types such as Porous Silicon (PS) Images, in order to ameliorate the metrological evaluation of their applications. In the case of digital Images, noise can produce difficulties in the diagnosis of Images details, such as edges and limits, should be preserved. Most algorithms can reduce or remove speckle noise, but they do not consider the conservation of these details. This paper describes in detail, the different techniques that focus mainly on the smoothing or elimination of speckle noise in Images, as the aim of this study is to achieve the improvement of this smoothing and elimination, which is directly related to different processes (such as the detection of interest regions). Furthermore, the description of these techniques facilitates the operations of evaluations and research with a more specific scope. This study initially covers the definition and modeling of speckle noise. Then we elaborated in detail the different types of filters used in this study, finally, five statistical parameters such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Structural Similarity Index (SSIM), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) are calculated, compared and the results are tabulated, common in filter evaluation processes. Trough the calculation of the statistical parameters, we can classify the filters in terms of perceptual quality by providing greater certainty.

In this paper, a general framework for Image contrast enhancement algorithm based on an optimization problem is presented. Through this optimization, the intensities can be better distributed. The algorithm is based on the facts that the histogram of the enhanced Image is close to the input Image histogram and uniform distribution, simultaneously. Based on this fact, we obtain a closed form optimal solution for the histogram of the enhanced Image. Experimental results in a wide range of Images demonstrate the high-performance of the proposed method.

Biometric-based techniques have emerged as the most promising option for individual recognition. This task is still a challenge for computer vision systems. Several approaches to adult Image recognition, which include the deep neural network and traditional classifier, have been proposed. Different Image condition factors such as expressions, occlusion, poses, and illuminations affect the facial recognition system. A reasonable amount of illumination variations between the gallery and probe Images need to be taken into account in adult Image recognition algorithms. In the context of adult Image verification, illumination variation plays a vital role and this factor will most likely result in misclassification. Different architectures and different parameters have been tested in order to improve the classification’ s accuracy. This proposed method contains four steps, which begin with Fuzzy Deep Neural Network Segmentation. This step is employed in order to segment an Image based on illumination intensity. Histogram Truncation and Stretching is utilized in the second step for improving histogram distribution in the segmented area. The third step is Contrast Limited Adaptive Histogram Equalization (CLAHE). This step is used to enhance the contrast of the segmented area. Finally, DCT-II is applied and low-frequency coefficients are selected in a zigzag pattern for illumination normalization. In the proposed method, AlexNet architecture is used, which consists of 5 convolutional layers, max-pooling layers, and fully connected layers. The Image is passed through a stack of convolutional layers after fuzzy neural representation, where we used filter 8 × 8. The convolutional stride is fixed to 1 pixel. After every convolution, there is a subsampling layer, which consists of a 2×2 kernel to do max pooling. This can help to reduce the training time and compute complexity of the network. The proposed scheme will be analyzed and its performance in accuracy and effectiveness will be evaluated. In this research, we have used 80, 400 Images, which are imported from two datasets-the Compaq and Poesia datasets-and used Images found on the Internet.

Background: Delivering the radiation dose to the target volume and minimizing the dose to normal tissues are the main objectives in radiotherapy. The aim of our study is to enhance the contrast of the portal Image to increase the accuracy of delineation of the organs in the irradiation field. Methods: The software was written based on local enhancement of the pixel values in Image matrix. The portal Images were digitized by charged coupled device (CCD) in compatible format to be read with this program. This program was applied as an m-file in MATLAB imaging tool box to the matrices of the portal Images. The imaging parameters before and after application of the program were compared. Results: The quantitative information of Images was obtained. Analysis of the mean and standard deviations of the results has shown that the difference of the criteria between two groups of the Images is significant (p< 0.01). In qualitative analysis, final Images scores were based on “special weight “. The result of this test confirms the superior quality of the post-processed Images from the professional view point. Conclusion: Superiority of final Images within the three studied parameters by the experts (superiority of lung Image, superiority of thorax and its soft tissue Images) can be used to increase the accuracy of the treatment set up and decrease the probability of normal tissue complications.

We propose a new approach for Image enhancement, denoising and restoration, using an anisotropic diffusion based on P-M model and L.V and al. equation, replacing the gradient by motion by mean curvature to detect noise direction for each degraded pixel locally, applying the gradient in Gaussian kernel term to restore the degraded pixels and adding a time term supporting the restoration process. For execution progress, the numerical discretization for the terms of PDE modeling (obtained by the approximation by difference finite volumes finite method, Taylor method and Simpsons improved method), concludes an algorithm treats noised Image regardless the noise type (salt-pepper or Gaussian or speckle) better than other filters based whether on anisotropic diffusion or total, shown in the experimental results (using MATLAB program), and demonstrated through PSNR and SSIM.