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Human vision is highly sensitive to perceive the contrast for interpreting images, so that the neurons of the brain will be excited to make recognition of the surroundings. In this study, the test patterns were inspired to reach the neurons excitation via the proposed pixel resonance concept, and then the quantum concept of physics was employed with random walk behaviour to analyse the test patterns for multiple interpretations. In simulation, similar alphabetic letters (C, O) and Chinese characters (?, ?) are selected. Subsequently, various extents of noise are added including the different mean values and related standard deviations for the two test patterns. The results showed that the corresponding resonance maps with various morphologies can generate multiple resonance outputs. Furthermore, the simulation results indicated that the peak signal-to-noise ratio curves match those of the resonance maps perceived by using human vision. Based on this approach, it is concluded that the proposed pixel resonance algorithm can effectively simulate the multiple interpretations of the human vision system. Further, a novel physical model was presented to generate multi-output patterns for a single image.