Live Demonstration: Supervised-learning-based Visual Quantification for Image Enhancement

Abstract

This demonstration showcases a framework of visual quantification for image enhancement where multivariate Gaussian (MVG) models are trained to assess image visibility. The visibility of an image is depicted by statistical features such as the contrast energy of the gray channel, yellow-blue channel, and red-green channel, average saturation, and gradients. The predicted visibility scores are then applied to define adaptive histogram equalization clip parameters for image enhancement. Finally, the hardware architecture is implemented on an FPGA to demonstrate the results for real-time image enhancement.