Image enhancement and dynamic range compression using novel intensity-specific stochastic resonance-based parametric image enhancement model

This paper presents a noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions. The application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated for such images. The new mathematical model addresses the shortcomings of earlier SR-based enhancement model by deriving parameters purely from input values (instead of input statistics). This model is specific to spatial domain pixel representation and operates on a revised iterative equation. This iterative processing is here applied selectively to the under-illuminated regions of the image, characterized as the De Vries-Rose (DVR) region of a human psychovisual model. The idea of suitably modifying the existing universal image quality index is also proposed for its participation in iteration termination, and to gauge the property of dynamic range compression. While the iterative algorithm is terminated using the revised image quality index, entropy maximization, and contrast quality of DVR region with constraints on perceptual quality, the performance of the proposed algorithm is also characterized by observing color enhancement and subjective scores on visual quality.