Adaptive wavelet-based contrast enhancement of IR imagery

This paper presents a suite of techniques called the Adaptive Wavelet-based Contrast Enhancement Method (AWCEM) for improving the subjective quality of an image for observation by a human. The central idea in these techniques is the space-varying stretching of the contrast of an image by enhancing or attenuating its detail wavelet transform coefficients. The degree of stretch is governed by a multi-resolution region of interest (ROI) mask which is generated by a low-level feature extraction mechanism. Intelligent image enhancement seeks to preserve and amplify target details while at the same time suppressing the background clutter. This calls for an enhancement mechanism that (i) is adaptive across the image, (ii) performs some form of low-level feature extraction and (iii) uses these features to control the level of contrast stretching. The proposed algorithms use a combination of the multiscale energies, edge strengths, texture and motion as the features. Local scale-space anomalies in the image seed the regions of interest. We present the suite of techniques within an interactive environment, the AWCEM Tool, which allows the user to choose and optimize the algorithm for the particular type of data encountered in the final application. The algorithm applies well to both single-channel IR and visual imagery.