Color Image Enhancement Method Based on Weighted Image Guided Filtering

A novel color image enhancement method is proposed based on Retinex to enhance color images under non-uniform illumination or poor visibility conditions. Different from the conventional Retinex algorithms, the Weighted Guided Image Filter is used as a surround function instead of the Gaussian filter to estimate the background illumination, which can overcome the drawbacks of local blur and halo artifact that may appear by Gaussian filter. To avoid color distortion, the image is converted to the HSI color model, and only the intensity channel is enhanced. Then a linear color restoration algorithm is adopted to convert the enhanced intensity image back to the RGB color model, which ensures the hue is constant and undistorted. Experimental results show that the proposed method is effective to enhance both color and gray images with low exposure and non-uniform illumination, resulting in better visual quality than traditional method. At the same time, the objective evaluation indicators are also superior to the conventional methods. In addition, the efficiency of the proposed method is also improved thanks to the linear color restoration algorithm.Comment: 15 page

An Improved Approach To Watercolor Reproduction By Profile Editing

Fine art is usually produced on paper or canvas as a one-of-a-kind artwork. Fine art may be reproduced in limited editions and put up for sale as art. Different printing technologies have been used in fine art reproduction such as lithography, screenprinting, and most recently inkjet. The research aspect of watercolor reproduction has been the question of how good is good enough. In this case, the artists demand the exact match between the original watercolor and its reproduction. While there are difficulties in quantifying the degree of color image match, the initial testing of watercolor reproduction using a colormanaged approach with an inkjet printer showed that there is a need to improve the reproduction quality. The objective of this study is to see if accuracy of watercolor reproduction can be improved by using profile editing tools. The significance of the research is the potential to achieve higher reproduction quality in watercolor by means of profile editing. In addition, we can put control back in the hands of content creators for limited editions. This research begins with a literature review. The review discusses how artworks are being digitized and reproduced by museums. It points out the wide adoption of International Color Consortium (ICC) color management practices in printing and publishing. It also covers how a color image match between an original and its reproduction is assessed quantitatively and qualitatively. The quantitative analyses of Macbeth ColorChecker between a generic ICC profile and a custom ICC profile were used to test first hypothesis, i.e., if there is any significant difference in measured color accuracy of watercolor reproductions between a generic ICC profile and a custom ICC profile. The results indicate that there is a significant difference in color accuracy of watercolor reproduction between using generic ICC profile and the custom ICC profile state findings. To our surprise, the custom ICC profile performed worse than the generic ICC profile. A possible cause of the large color differences was attributed to the accuracy of the scanner profile. A paired comparison method was used to test the second hypothesis, i.e., if there is any significant visual difference in color accuracy of watercolor reproductions between an unedited ICC profile and an edited ICC profile. The results indicate that there is no significant difference in color accuracy of watercolor reproduction between an unedited ICC profile and an edited ICC profile state findings. To our surprise once again, edited profiles did not perform color matching any better than unedited profiles. A major factor is that editing of tone reproduction and gray balance are treated as two separate events in the profile editing process. In fact, tone reproduction and gray balance are dependent on each other

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

Probing emotional influences on cognitive control: an ALE meta-analysis of cognition emotion interactions

Increasing research documents an integration of cognitive control and affective processes. Despite a surge of interest in investigating the exact nature of this integration, no consensus has been reached on the precise neuroanatomical network involved. Using the Activation Likelihood Estimation meta-analysis method, we examined 43 functional Magnetic Resonance Imaging (fMRI) studies (total number of foci = 332; total number of participants, N =820) from the literature that have reported significant interactions between emotion and cognitive control. Meta-analytic results revealed that concurrent emotion (relative to emotionally neutral trials) consistently increased neural activation during high relative to low cognitive control conditions across studies and paradigms. Specifically, these activations emerged in regions commonly implicated in cognitive control such as the lateral prefrontal cortex (inferior frontal junction, inferior frontal gyrus), the medial prefrontal cortex, and the basal ganglia. In addition, some areas emerged during the interaction contrast that were not present during one of the main effects and included the subgenual anterior cingulate cortex and the precuneus. These data provide new evidence for a network of cognition emotion interaction within a cognitive control setting. The findings are discussed within current theories of cognitive and attentional control