A computer implemented noise reduction method for a X-ray computed tomography system characterized in that the method comprises: using a data acquisition system to convert signals output from an X-ray detector of the X-ray computed tomography system into voltage signals, said data acquisition system amplifying the voltage signals and converting the amplified signals into digital measurement signals; estimating a noise variance for each digital measurement signal based on a predetermined noise model, wherein the noise variance is estimated for each digital measurement signal after logarithmic conversion processing, wherein the predetermined noise model is defined by an average value of the digital measurement signals and a known electric noise value and wherein the estimated variance of the predetermined noise-model is 1 + V e / I I , where I is the average value of the digital measurement signals while V e is the known electric noise value; generating a discrete filter kernel of a noise-equalizing Gaussian filter for each of the digital measurement signals based on the estimated noise variance; and applying the discrete filter kernel to a corresponding one of the digital measurement signals to generate filtered measurement signals wherein the discrete filter kernel is applied to digital measurement signals prior to logarithmic conversion processing, wherein the discrete filter kernel has a kernel size determined based on a standard deviation obtained from a kernel variance adaptively decided based on the noise variance. using a data acquisition system to convert signals output from an X-ray detector of the X-ray computed tomography system into voltage signals, said data acquisition system amplifying the voltage signals and converting the amplified signals into digital measurement signals; estimating a noise variance for each digital measurement signal based on a predetermined noise model, wherein the noise variance is estimated for each digital measurement signal after logarithmic conversion processing, wherein the predetermined noise model is defined by an average value of the digital measurement signals and a known electric noise value and wherein the estimated variance of the predetermined noise-model is 1 + V e / I I , where I is the average value of the digital measurement signals while V e is the known electric noise value; generating a discrete filter kernel of a noise-equalizing Gaussian filter for each of the digital measurement signals based on the estimated noise variance; and applying the discrete filter kernel to a corresponding one of the digital measurement signals to generate filtered measurement signals wherein the discrete filter kernel is applied to digital measurement signals prior to logarithmic conversion processing, wherein the discrete filter kernel has a kernel size determined based on a standard deviation obtained from a kernel variance adaptively decided based on the noise variance. where I is the average value of the digital measurement signals while V e is the known electric noise value; generating a discrete filter kernel of a noise-equalizing Gaussian filter for each of the digital measurement signals based on the estimated noise variance; and applying the discrete filter kernel to a corresponding one of the digital measurement signals to generate filtered measurement signals wherein the discrete filter kernel is applied to digital measurement signals prior to logarithmic conversion processing, wherein the discrete filter kernel has a kernel size determined based on a standard deviation obtained from a kernel variance adaptively decided based on the noise variance. <EOS>