Most state-of-the-art techniques focus primarily on daytime surveillance, and limited research has been performed on nighttime monitoring. Surveillance is often more important in darker environments since many activities of interest often occur at night. But, nighttime imagery presents its challenges: they are mostly monochrome and very noisy. Night vision systems are also affected in the presence of a bright light or glare off a shiny surface. Color imagery has several benefits over grayscale imagery. The human eye can discriminate broad spectrum of colors but is limited to about 100 shades of gray. Also, color drives visual attention and aids in better understanding of the scene. Moreover, context information of the scene affects the way humans distinguish and recognize things. The essential step of a coloring process is the choice of an appropriate color image model and color mapping scheme. To enhance relevant information of nighttime images, a color mapping or color transfer technique is employed. The paper proposes a robust pixel-based color transfer architecture that maps the color characteristics of the daytime images to the nighttime images. The architecture is also capable of compensating for image registration issues encountered during acquisition. A visual analysis of the results demonstrate that the proposed method performs better in comparison to the state-of-the-art methods and is robust to different imaging sensors.
The primary objective of enhancement is to improve the contrast an image, that the outcome image is more appropriate than the original image for the given application. One of the simplest, computationally effective and most used empirical algorithms that may improve overall contrast is the class (linear stretching and non-linear stretching) of stretching methods. However, linear and non-linear stretching suffer from several issues, for instance, a low-contrast effect by organizing intensities or an over-brightness effect by super-imposing intensities. The goal of this paper is to present new techniques for image contrast enhancement: (1) a bi-non-linear contrast-stretching algorithm, (2) the optimized combination of linear contrast and non-linear contrast stretching algorithms, and (3) the optimized combination of a linear contrast, a non-linear contrast stretching and a local histogram equalization algorithm. Computer simulations on publicly available Thermal Focus Image Database and the Tufts Face Database show that the proposed methods increase the dynamic image range and demonstrate a significantly improved global and local contrast by taking the most exquisite details and edges. In addition, the simulation results show that the proposed method well correlates with subjective evaluations of image quality. The presented concept is useful in guiding the future design of cutting-edge image enhancement methods.