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This paper presents a new approach to represent an image by "verge points," which are defined as high-curvature points on the image surface. This representation offers a compact and reversible way to preserve the essence of the original image. Various applications, such as compression, edge detection, image enhancement, and image editing, can be achieved based on this representation. In this paper, the whole procedure for verge point representation is presented. Based on these verge points, image reconstruction can be easily achieved via iterative linear interpolation. These extracted verge points with compatible properties are further linked into verge curves to offer more compact representation. Progressive representation is also developed based on a multiscale extraction scheme. Some potential applications are then presented to demonstrate the versatility of this representation. [ABSTRACT FROM AUTHOR]

The logarithmic image processing (LIP) model is a mathematical theory that provides new operations for image processing. The contrast definition has been shown to be consistent with some important physical laws and characteristics of human visual system. In this paper, we establish an information-theoretic interpretation of the contrast definition. We show that it can be expressed as a combination of the relative entropy and Shannon's information content. Based on this new interpretation, we propose an adaptive algorithm for enhancing the contrast and sharpness of noisy images. [ABSTRACT FROM AUTHOR]