Correcting registration errors in digital printing

Patent metadata

Field	Value
Publication number	US-2021182001-A1
Application number	US-202017106245-A
Country	US
Kind code	A1
Filing date	Nov 30, 2020
Priority date	Dec 11, 2019
Publication date	Jun 17, 2021
Grant date	—

Abstract

a method for correcting an error in image printing, the method includes receiving a reference digital image (RDI). Based on a predefined selection criterion, one or more regions in the RDI that are suitable for use as anchor features for sensing the error, are selected. A digital image (DI) acquired from a printed image of the RDI, is received and the one or more regions are identified in the DI. Based on the anchor features of the DI, the error is estimated in the printed image. A correction that, when applied to the DI, compensates for the estimated error, is calculated. The estimated error is corrected in a subsequent digital image (SDI) to be printed, and the SDI having the corrected error, is printed.

First claim

1 . A method for correcting an error in image printing, the method comprising:
receiving a reference digital image (RDI); selecting, based on a predefined selection criterion, one or more regions in the RDI that are suitable for use as anchor features for sensing the error; receiving a digital image (DI) acquired from a printed image of the RDI, and identifying the one or more regions in the DI; estimating, based on the anchor features of the DI, the error in the printed image; calculating a correction that, when applied to the DI, compensates for the estimated error; correcting the estimated error in a subsequent digital image (SDI) to be printed; and printing the SDI having the corrected error.

2 . The method according to claim 1 , wherein the error comprises a registration error, and wherein selecting the one or more regions comprises producing multiple versions of the RDI having respective registration errors.

3 . The method according to claim 2 , wherein producing the multiple versions comprises applying to at least part of at least one of the regions, one or more errors selected from a list consisting of: (a) flipping in one or more axes, (b) rotating in one or more axes, (c) image blurring, and (d) adding noise, and wherein selecting the one or more regions comprises training a neural network using the multiple versions.

4 . The method according to claim 1 , wherein estimating the error comprises training a neural network to detect the error in the one or more regions, and wherein training the neural network comprises producing multiple versions of the RDI having respective errors.

5 . The method according to claim 4 , wherein the error comprises a registration error, and wherein training the neural network comprises comparing, in a selected region, between a predefined registration error produced in the selected region, and an output registration error estimated by the neural network, and applying, to the predefined and the output registration errors, a loss function, and comparing an output of the loss function to a threshold.

6 . The method according to claim 4 , wherein the RDI comprises at least a first reference color image (RCI) having a first color, and a second RCI having a second color, and wherein producing the multiple versions comprises shifting, in the region, at least part of the first RCI relative to the second RCI.

7 . The method according to claim 4 , wherein training the neural network comprises training a multi-layered convolutional neural network comprising at least five layers.

8 . The method according to claim 1 , wherein the error comprises a registration error, and wherein estimating the registration error comprises estimating an image-to-substrate (ITS) registration error by (i) measuring, in the RDI, a distance between at least one of the anchor features and a given region at an edge of the printed image, and (ii) estimating, in the printed image of the DI, a variation of the distance.

9 . The method according to claim 1 , wherein the error comprises a registration error, wherein each of the RDI and DI comprises at least first and second colors, and wherein estimating the registration error comprises estimating, in at least one of the regions of the DI, a color-to-color (CTC) registration error between the first and second colors by (i) measuring, in at least one of the regions of the RDI image, a given distance between the first color and the second color, and (ii) estimating, in the printed image of the DI, a variation of the given distance.

10 . The method according to claim 1 , wherein the error comprises at least one of: (a) a shift of a given pattern of the printed image relative to a position of the given pattern in the RDI, (b) a missing pattern, (c) a non-uniform thickness of a pattern in the printed image, (d) a non-uniform width of the pattern in the printed image, (e) a deviation in a profile of one or more colors of the printed image, and (f) a deviation in a linearity of one or more colors of the printed image.

11 . A system for correcting an error in image printing, the system comprising:
a processor, which is configured to:
receive a reference digital image (RDI);
select, based on a predefined selection criterion, one or more regions in the RDI that are suitable for use as anchor features for sensing the error;
receive a digital image (DI) acquired from a printed image of the RDI, and identify the one or more regions in the DI;
estimate, based on the anchor features of the DI, the error in the printed image;
calculate a correction that, when applied to the DI, compensates for the estimated error; and
correct the estimated error in a subsequent digital image (SDI) to be printed; and
a printing subsystem configured to print the SDI having the corrected error.

12 . The system according to claim 11 , wherein the error comprises a registration error, and wherein the processor is configured to produce multiple versions of the RDI having respective registration errors.

13 . The system according to claim 12 , wherein the processor is configured to train a neural network using the multiple versions by applying to at least part of at least one of the regions, one or more errors selected from a list consisting of: (a) flipping in one or more axes, (b) rotating in one or more axes, (c) image blurring, and (d) adding noise.

14 . The system according to claim 11 , wherein the processor is configured to train a neural network to detect the error in the one or more regions, and to produce multiple versions of the RDI having respective errors.

15 . The system according to claim 14 , wherein the error comprises a registration error, and wherein the processor is configured to: (i) compare, in a selected region, between a predefined registration error produced in the selected region, and an output registration error estimated by the neural network, (ii) apply, to the predefined registration error and to the output registration error, a loss function, and (iii) compare an output of the loss function to a threshold.

16 . The system according to claim 14 , wherein the RDI comprises at least a first reference color image (RCI) having a first color, and a second RCI having a second color, and wherein the processor is configured to shift, in the region, at least part of the first RCI relative to the second RCI.

17 . The system according to claim 14 , wherein the processor is configured to train a multi-layered convolutional neural network comprising at least five layers.

18 . The system according to claim 11 , wherein the error comprises a registration error, and wherein the processor is configured to estimate an image-to-substrate (ITS) registration error, by (i) measuring, in the RDI, a distance between at least one of the anchor features and a given region at an edge of the printed image, and (ii) estimating, in the printed image of the DI, a variation of the distance.

19 . The system according to claim 11 , wherein the error comprises a registration error, wherein each of the RDI and DI comprises at least first and second colors, and wherein the processor is configured to estimate, in at least one of the regions of the DI, a color-to-color (CTC) registration error between the first and second colors by (i) measuring, in at least one of the regions of the RDI image, a given distance between the first color and the second color, and (ii) estimating, in the printed image of the DI, a variation of the given distance.

20 . The system according to claim 11 , wherein

Assignees

• Landa Corp Ltd

Inventors

• Levant Boris

Classifications

• G06F3/121

  Facilitating exception or error detection and recovery, e.g. fault, media or consumables depleted · CPC title

• H04N1/506

  Reproducing the colour component signals picture-sequentially, e.g. with reproducing heads spaced apart from one another in the subscanning direction · CPC title

• G06F3/1234 Primary

  Errors handling and recovery, e.g. reprinting (G06F3/1261 takes precedence) · CPC title

• G06F3/1282

  High volume printer device · CPC title

• G06T2207/30144

  Printing quality · CPC title