On Using and Improving Gradient Domain Processing for Image Enhancement

Abstract

Gradient domain image processing is a type of image manipulation that directly processes the derivatives of an image (i.e. gradient) instead of its pixel values. This involves a two step procedure where the image gradients are first processed in a task-specific manner based on the desired enhancement, followed by a reconstruction step that estimates the new pixels values from the modified gradient field. Since its adoption nearly a decade ago, there has been several successful examples of using gradient domain processing for image enhancement tasks ranging from texture transfer, gradient boosting to saliency sharpening and data fusion. This dissertation continues this trend of gradient domain image enhancement and offers three contributions in this area.