In image communication systems, subsampling, can be used to decrease the transmission rate of images. One common technique is to subsample the chrominance information. This process, however, introduces visible color artifacts. This paper develops an algorithm for enhancing the subsampled image data, thereby eliminating the artifacts.

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.

The method of Tikhonov regularization has been widely used to form well-posed inverse problems in low-level vision. The application of this technique usually results in a least squares approximation or a spline fitting of the parameter of interest. This is often adequate for estimating smooth parameter fields. However, when the parameter of interest has discontinuities the estimate formed by this technique will smooth over the discontinuities. Several techniques have been introduced to modify the regularization process to incorporate discontinuities. Many of these approaches however, will themselves be ill-posed or ill-conditioned. This paper presents a technique for incorporating discontinuities into the reconstruction problem while maintaining a well-posed and well-conditioned problem statement. The resulting computational problem is a convex functional minimization problem. This method is compared to previous approaches and examples are presented for the problems of reconstructing curves and surfaces with discontinuities and for estimating image data. Computational issues arising in both analog and digital implementations are also discussed.>