Perceptually Motivated Automatic Color Contrast Enhancement

Abstract

We address the problem of contrast enhancement for color images. Methods directly derived from gray-level enhancement such as histogram equalization produce significant artifacts, including severe color shifts. Other enhancement techniques that are derived from the Retinex theory may suffer from strong `halo' effects. Our method to enhance images is inspired from the Retinex theory and tries to mimic human color perception. The method helps in achieving color constancy and also results in color contrast enhancement. We express the intensity as a product of illumination and reflectance and estimate these separately. Enhancement is then applied to the illuminant component only. Non-local means filter is used to estimate the illuminant and then the enhancement of the illumination is performed automatically without any manual intervention and multiplied back by the reflectance to obtain enhancement. We compare our results with those from other enhancement techniques and with results from commercial software packages such as PhotoFlair® that uses multi-scale retinex with color restoration (MSRCR) and Picasa¿ and observe that our results are consistently 'visually better'. Finally, we perform a statistical analysis of our results and quantitatively show that our approach produces effective and substantial image enhancement. This is validated by ratings from human observers.