Color Palette Restoration

Abstract

To build inexpensive image display hardware, designers severely restrict the number of colors which can be displayed simultaneously on a device. A color image of a natural scene typically has tens or hundreds of thousands of different colors in it. To display such an image on a reduced palette display device requires the image to be mapped from the full resolution color space to the reduced color space allowed by the device. Over the years, many algorithms for both selecting the reduced palette and for mapping the full resolution color space image to the reduced palette have been developed. This mapping is lossy and degrades the original image information. If the reduced color space image is processed using common image-processing algorithms, or if the image is viewed on a device with a higher resolution color space, this degradation is very noticeable. In these cases, much better results can be obtained by first reconstructing a full color image from the reduced palette image. This creates a need for a palette restoration algorithm. This paper develops an algorithm for reconstructing high-color-resolution image data from reduced color palette images. The algorithm is based on stochastic regularization using a non-Gaussian Markov random field model for the image data. This results in a constrained optimization algorithm which is solved using an iterative constrained gradient descent computational algorithm. Results of the proposed palette restoration algorithm have indicated that it is effective for the reconstruction of palettized images. Visual results of several experiments are presented.