In this paper, we develop new techniques for monitoring image processes under a fairly general setting with spatially correlated pixels in the image. Monitoring and handling the pixels directly is infeasible due to an extremely high image resolution. To overcome this problem, we suggest control charts that are based on regions of interest. The regions of interest cover the original image which leads to a dimension reduction. Nevertheless, the data are still high-dimensional. We consider residual charts based on the generalized likelihood ratio approach. Existing control statistics typically depend on the inverse of the covariance matrix of the process, involving high computing times and frequently generating instable results in a high-dimensional setting. As a solution of this issue, we suggest two further control charts that can be regarded as modifications of the generalized likelihood ratio statistic. Within an extensive simulation study, we compare the newly proposed control charts using the median run length as a performance criterion.