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There are various image filtering approaches in computer vision and image processing that are effective for some types of noise, but they invariably make certain assumptions about the properties of the signal and/or noise which lack the generality for diverse image noise reduction. This study describes a novel generalised guided image filtering method with the reference image generated by signal subspace projection (SSP) technique. It adopts refined parallel analysis with Monte Carlo simulations to select the dimensionality of signal subspace in the patch-based noisy images. The noiseless image is reconstructed from the noisy image projected onto the significant eigenimages by component analysis. Training/test image are utilised to determine the relationship between the optimal parameter value and noise deviation that maximises the output peak signal-to-noise ratio (PSNR). The optimal parameters of the proposed algorithm can be automatically selected using noise deviation estimation based on the smallest singular value of the patch-based image by singular value decomposition (SVD). Finally, we present a quantitative and qualitative comparison of the proposed algorithm with the traditional guided filter and other state-of-the-art methods with respect to the choice of the image patch and neighbourhood window sizes.