Contextual and Variational Contrast Enhancement

A Variational Histogram Equalization Method for Image Contrast Enhancement

The main aim of this talk is to discuss a histogram equalization algorithm for image contrast enhancement. Our idea is to propose a variational approach containing an energy functional to determine a local transformation such that the histogram can be redistributed locally, and the brightness of the transformed image can be preserved. In order to minimize the differences among the local transfo...

[Contrast enhancement].

Variational approaches to super-resolution with contrast enhancement and anisotropic diffusion

We present super-resolution methods that enhance image contrast and perform anisotropic diffusion simultaneously. Since we solve the super-resolution problem by encouraging Mach-band profiles while incorporating anisotropic diffusion, our technique not only reconstructs a high-resolution image from several overlapping noisy low-resolution images, but also enhances edges and image contrast while...

Contrast Enhancement at Borders Spatial Contrast Enhancement at Borders

meanings, we use context to select the meaning intended by the speaker. But context is involved at an even earlier level in speech perception because it allows us to recognize sounds as words. We perceive spoken words as a sequence of vowel and consonant sounds, that is, as a sequence of phonemes. Perceptually, each phoneme seems fixed, but acoustically, a phoneme can vary considerably dependin...

An Algorithmic Analysis of Variational Models for Perceptual Local Contrast Enhancement

Color cast cancellation and local contrast enhancement are very important problems in computer vision. In this paper we review the algorithm proposed by Palma-Amestoy et al. [A perceptually inspired variational framework for color enhancement, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (2009), pp. 458–474], present results and evaluate the impact of a change in the param...