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An algorithm for detection and individually substitution of bad pixels for further restoration of an image in the presence of such outliers without altering overall image texture is presented. This work presents three phases concerning image processing: bad pixel identification and mapping by means of linear regression and the coefficient of determination of the pixel output as a function of exposure time, local correction of the linear and angular coefficients of the outlier pixels based on their neighbourhood and, finally, image restoration. Simulation and experimental data were used as means of code benchmarking, showing satisfactory results.

A novel algorithm for bad pixel detection and correction to improve quality and stability of geometric measurements