A closed-form correlation model of oriented bandpass natural images beyond adjacent responses

Sinno, Zeina
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Building natural scene statistical models is crucial for a large set of applications starting from the design of faithful image and video quality metrics to image enhancing techniques. Most predominant statistical models of natural images characterize univariate distributions of divisively normalized bandpass responses or wavelet-like decomposition of them. Previous models focusing on these bandpass natural responses offer optimized solutions to numerous problems in image processing however, these models have not focused on finding a closed-form quantative model capturing the bivariate natural statistics. Towards the efforts for filling this gap, Su et. al recently modeled spatially horizontally neighboring bandpass image responses on multiple scales; however, the latter work did not cover the response of distant bandpass image responses with various spatial orientations. This work builds on Su. et al 's model and extends the closed-form correlation model to cover distant bandpass image responses, up to a distance of ten pixels; with multiple spatial orientations, encompassing all the discrete spatial angles for the lastly-mentioned distances on multiple scales.