Unsupervised Enhancement and Web Tool for Perceptually Invisible Type Degraded Image

Severe contrast degradation in an image result in a substantial loss of crucial visible information. A novel unsupervised image enhancement algorithm is proposed in this paper to unravel hidden visual details in perceptually invisible images. In this paper, new algorithms are designed to solve the range localization problem. The proposed algorithm intends to expand the image range to maximize its intensity level utilization. Outliers present in an image intensity range are truncated based on their power distribution. Based on the type of input image, maxima-minima, or frequency-based expansion of truncated levels, is exercised to avoid over-enhancement. Two novel parameters, namely the transmission map and zero-frequency spread, are coined. The visually enhanced result successfully exhibits the camouflaged information and avoids over-enhancement. Consumer gadgets, such as cameras, have difficulty taking pictures in very dark and cloudy settings. The suggested algorithm can improve low-contrast photos that are both very dark and severely attenuated. The unsupervised nature of the proposed algorithm makes it suitable for application in the field of consumer electronics like mobile phones and digital cameras. A consumer-focused, platform-independent app is created and made available to the general public for additional study.