Image Enhancement using Logarithmic Image Processing Model

Abstract

Image enhancement process consist of a collection of techniques that seek to improve the visual appearance of an image, or to convert the image to a form better suited for analysis by a human visual system or for machine perception. In many practical situations, the illumination of a scene is changing with space and time. It is not unusual to have images having brightly lit and shadowed portions in the same scene demanding information extraction from the entire image. For example in the instance of space images over mountainous terrain you find brightly lit portions facing the sun while the other portion remain in the dark. Techniques based on homomorphic filtering are useful in these cases. However, these are based on spatial frequency filtering and tend to perform poorly in low SNR portion of the image.Up to now, image processing and image analysis have borrowed their basic tools from functional analysis like Fourier filtering, differential and integral calculus etc, these tools are very efficient when they are put in a well defined algebraic frame, unfortunately all images does not have the same structure. The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded range. The LIP model is consistent with the multiplicative transmittance and reflectance image formulation model, and with some important laws and characteristic of human brightness perception. This article addresses the Lee's image enhancement algorithm in a different approach. This approach, based on LIP model can simultaneously enhance the overall contrast and the sharpness of an image. The algorithm is shown to be capable of enhancing the details in the dark as well as bright areas of digital image and at the same time obtains improved overall contrast.