Some Psychosocial Determinants of Cyber-Intimate Image Diffusion: A Cross-Sectional Study among In-School Deaf Adolescents

This study determined some psychosocial variables on cyber-intimate image diffusion among in-school deaf adolescents from two states in North-Central Nigeria. A descriptive research design and a multistage sampling procedure were employed in identifying 186 deaf adolescents who participated in the study. Two hypotheses were formulated for the study. The theory of planned behaviour was adopted as a framework for the study. Data generated through the questionnaire were analysed using descriptive statistics of frequency count and simple percentage as well as inferential statistics of bivariate correlation and t-test at .05 level of significance. Sixty-five percent of the respondents were aged 16-19. All respondents used WhatsApp and 78% had a Facebook profile. Findings revealed a positive significant relationship between emotional intelligence (r = .489; p < 0.05), self-esteem (r = .530; p < 0.05) and cyber-intimate image diffusion. Also, significant differences were found between Christian and Muslim respondents (t = 11.250; p < 0.05); low and high emotional intelligence (t = -2.412; p < 0.05); low and high self-esteem (t = -11.958; p < 0.05) with regard to cyber-intimate intimate image diffusion. The study concludes that emotional intelligence and self-esteem are determinants of cyber-intimate image diffusion among deaf adolescents. This study, therefore, recommends a synergy between parents of the deaf and professionals working with deaf adolescents to develop modalities that enhance psychological wellbeing and reduce cyber-intimate image diffusion among deaf adolescents.

Automatic Choice of Denoising Parameter in Perona-Malik Model

In this work, we propose a no-reference method for automatic choice of the parameters of Perona-Malik image diffusion algorithm for the problem of image denoising. The idea of the approach it to analyze and quantify the presence of structures in the difference image between the noisy image and the processed image as the mutual information value. We apply the proposed method to photographic images and to retinal images with modeled Gaussian noise with different parameters and analyze the effects of no-reference parameter choice compared to the optimal results. The proposed algorithm shows the effectiveness of no-reference parameter choice for the problem of image denoising.