Principal component analysis (PCA) is a well-known tool in image processing, especially in dimension reduction schemes. Since using PCA is based on the vector representation of the image, the spatial locality of pixels in the image is not considered. However, generalized PCA (GPCA) could be applied to images in two dimensional spaces. Both schemes do not consider the noisy case of signals. Noise adjusted PCA (NAPCA) tries to find new coordinates for signal representation based on signal to noise ratio (SNR) maximization. In this paper we generalized noise adjusted GPCA to benefit the advantage of GPCA and SNR maximization case of NAPCA in two dimensional spaces. The experimental results on the huge databases show its reliability.