A method for reducing noise in a sequence of fluoroscopic images acquired by an X-ray detector, an image comprising one or more lines, an image being represented by curvelet coefficients c t,s,θsb,i,j, where s is the scale of the image, θ sb its sub-band linked to the angle of a line in the image and (i,j) its relative position, the method being characterized in that it comprises in combination at least the following steps: • detection of the lines in an image in the curvelet domain, using the curvelet coefficients sub-band by sub-band and by determining the maximum response of a filter associated with a sub-band θ sb, and the detected lines are stored in memory, • locally matching the detected lines, • considering a set of curvelet coefficients, testing whether a curvelet coefficient being considered belongs to a line detected at the time t or at an earlier time, • if the curvelet coefficient belongs to a detected line, testing whether the line, to which the coefficient belongs, is matched to a line detected at an earlier time, if this is the case, then the curvelet coefficient is categorized in a first set, • if the coefficient is not detected, or if the curvelet coefficient belongs to a line detected but non matched to a line detected at an earlier time, then forming at least a second set, • applying a first coefficient for time-domain filtering the curvelet coefficients belonging to the first set and at least a second coefficient for time-domain filtering the curvelet coefficients belonging to the second set, • determining the denoised image using the time-domain filtered curvelet coefficients. • detection of the lines in an image in the curvelet domain, using the curvelet coefficients sub-band by sub-band and by determining the maximum response of a filter associated with a sub-band θ sb, and the detected lines are stored in memory, • locally matching the detected lines, • considering a set of curvelet coefficients, testing whether a curvelet coefficient being considered belongs to a line detected at the time t or at an earlier time, • if the curvelet coefficient belongs to a detected line, testing whether the line, to which the coefficient belongs, is matched to a line detected at an earlier time, if this is the case, then the curvelet coefficient is categorized in a first set, • if the coefficient is not detected, or if the curvelet coefficient belongs to a line detected but non matched to a line detected at an earlier time, then forming at least a second set, • considering a set of curvelet coefficients, testing whether a curvelet coefficient being considered belongs to a line detected at the time t or at an earlier time, • if the curvelet coefficient belongs to a detected line, testing whether the line, to which the coefficient belongs, is matched to a line detected at an earlier time, if this is the case, then the curvelet coefficient is categorized in a first set, • if the coefficient is not detected, or if the curvelet coefficient belongs to a line detected but non matched to a line detected at an earlier time, then forming at least a second set, • if the curvelet coefficient belongs to a detected line, testing whether the line, to which the coefficient belongs, is matched to a line detected at an earlier time, if this is the case, then the curvelet coefficient is categorized in a first set, • if the coefficient is not detected, or if the curvelet coefficient belongs to a line detected but non matched to a line detected at an earlier time, then forming at least a second set, • applying a first coefficient for time-domain filtering the curvelet coefficients belonging to the first set and at least a second coefficient for time-domain filtering the curvelet coefficients belonging to the second set, • determining the denoised image using the time-domain filtered curvelet coefficients.