Estimating intrinsic image from successive images by solving underdetermined and overdetermined systems of the dichromatic model

Estimating an intrinsic image from a sequence of successive images taken from an object at different angles of illumination can be used in various applications such as objects recognition, color classification, and the like; because, in so doing, it can provide more visual information. Meanwhile, according to the well-known dichromatic model, each image can be considered a linear combination of three components, including intrinsic image, shading factor, and specularity. In this study, at first, two simple independent constrained and parallelized quadratic programming steps were used for computing values of the shading factor and the specularity of each successive of images. In the algorithm mentioned above, only the mean and standard deviation of three channels for each pixel are required to solve the underdetermined problem of the dichromatic model equations. Then, the singular value decomposition method was used to estimate a unique intrinsic image through the values of the shading factor and the specularity of each of the images that constitute an overdetermined problem. The results of the successive reconstructed images using the estimated unique intrinsic image showed an increase in the visual assessment quality and color gamut of the final images.