# US 2014/0193097 A1 · Method and apparatus for blending images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0193097 A1 - Document ID: US20140193097A1 - Status: Published application - Kind: A1 - Application number: 14/202,798 - Filed: 2014-03-10 - Published: 2014-07-10 - Earliest claimed: 2009-09-23 - Assignees: Samsung Electronics Co., Ltd. - Applicants: Samsung Electronics Co., Ltd. - Inventors: Sung-Chan PARK; Hyun-Hwa OH; Jae-Hyun KWON; Seong-Deok LEE; Won-Hee CHOE - Web page: https://patentgrep.com/patent/US20140193097A1 ## CPC - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06T 2207/20208: High dynamic range [HDR] image processing - G06T 2207/20221: Image fusion; Image merging - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A method and apparatus may include aligning a reference image and an input image through ROI-based image alignment; detecting an ROI associated with an incompletely aligned region in the aligned image; and blending the aligned reference image and input image by using a plurality of weights which are attenuated according to a spatial distance from a boundary of the detected ROI. In addition, the method and apparatus may include aligning a reference image and an input image through image alignment based on an nth region of interest (ROI) which is detected at a previous stage; detecting another ROI associated with an incompletely aligned region in the aligned image; and blending the aligned image in the other ROI and an (n+1)th blended image, which is input from a next stage, in a region outside the other ROI, and outputting the last blended image to the previous stage. ## Claims ### Claim 1 A method for blending images, comprising: aligning a reference image and an input image through region of interest (ROI)-based image alignment; detecting an ROI associated with an incompletely aligned region in the aligned image; and blending the aligned input image and reference image by using a plurality of weights which are attenuated according to a spatial distance from a boundary of the detected ROI. ### Claim 2 (depends on 1) The method of claim 1, wherein blending the aligned input image and reference image comprises blending two images by taking the reference image in the ROI and the input image in a region outside the ROI. ### Claim 3 (depends on 1) The method of claim 1, wherein detecting the ROI comprises detecting a ghost. ### Claim 4 (depends on 1) The method of claim 1, wherein the weights are more greatly attenuated at an edge of an image than at other portions of the image. ### Claim 5 (depends on 1) The method of claim 1, wherein the weights are attenuated depending on a difference in brightness level which occurs in the boundary of the ROI. ### Claim 6 A method for blending images, comprising: aligning a reference image and an input image through image alignment based on an nth region of interest (ROI) which is detected at a previous stage; detecting another ROI associated with an incompletely aligned region in the aligned image; blending the aligned image in the other ROI and an (n+1)th blended image, which is input from a next stage, in a region outside the other ROI; and outputting the last blended image to the previous stage. ### Claim 7 (depends on 6) The method of claim 6, wherein detecting the other ROI comprises detecting a ghost. ### Claim 8 (depends on 6) The method of claim 6, further comprising outputting an (n+1)th ROI, which is an overlapped portion between the nth ROI and the other ROI, to a next stage. ### Claim 9 (depends on 6) The method of claim 6, wherein blending the aligned image and the (n+1)th blended image comprises blending the two images by using a plurality of weights which are attenuated according to a spatial distance from a boundary of the other ROI. ### Claim 10 (depends on 9) The method of claim 9, wherein the weights are more greatly attenuated at an edge of an image than at the other portions of the image. ### Claim 11 (depends on 9) The method of claim 9, wherein the weights are attenuated depending on a difference in brightness level which occurs in the boundary of the other ROI. ### Claim 12 An image blender, comprising: an image aligning unit configured to align a reference image and an input image through ROI-based image alignment; an ROI detecting unit configured to detect an ROI associated with an incompletely aligned region by detecting motion in an aligned image output from the image alignment unit; a weight calculating unit configured to calculate a plurality of weights which are attenuated depending on a spatial distance from a boundary of the ROI detected by the ROI detecting unit; and an image blending unit comprising a processor, the image blending unit configured to blend an aligned image output from the image aligning unit and a reference image according to the weights which are calculated by the weight calculating unit. ### Claim 13 (depends on 12) The image blender of claim 12, wherein the ROI detecting unit comprises a ghost detecting unit configured to detect a ghost. ### Claim 14 An image blender, comprising: an image aligning unit configured to align a reference image and an input image through image alignment, based on an nth ROI which is detected at a previous stage; an ROI detecting unit configured to detect another ROI associated with an incompletely aligned region, based on motion detection in an aligned image output from the image aligning unit; and an image blending unit comprising a processor, the image blending unit configured to: blend the aligned input image in the other ROI and an (n+1)th blended image, which is input from a next stage, in a region outside the other ROI; and output the last blended image to the previous stage. ### Claim 15 (depends on 14) The image blender of claim 14, wherein the ROI detecting unit comprises a ghost detecting unit configured to detect ghosts, based on motion detection in the aligned image output from the image aligning unit. ### Claim 16 (depends on 14) The image blender of claim 14, wherein the ROI detecting unit is further configured to output an (n+1)th ROI, which is an overlapped portion between the nth ROI and the other ROI, to a next stage. ### Claim 17 (depends on 14) The image blender of claim 14, further comprising a weight calculating unit configured to calculate a plurality of weights, the image blending unit being further configured to blend the two images using the plurality of weights, the plurality of weights being attenuated depending on a spatial distance from a boundary of the other ROI. ### Claim 18 An image blending apparatus, comprising: is a plurality of image blenders, at least two of the plurality of image blenders comprising: an image aligning unit configured to align a reference image and an input image through image alignment, based on an nth ROI which is detected at a previous stage; an ROI detecting unit configured to detect another ROI associated with an incompletely aligned region, based on motion detection in an aligned image output from the image aligning unit; and an image blending unit comprising a processor, the image blending unit configured to: blend the aligned image in the other ROI and an (n+1)th blended image, which is input from a next stage, in a region outside the other ROI; and output the last blended image to the previous stage. ### Claim 19 (depends on 18) The image blending apparatus of claim 18, wherein the plurality of image blenders are cascaded. ### Claim 20 (depends on 19) The image blending apparatus of claim 19, wherein an initial ROI of the first stage image blender is set to a low-brightness region, or a high-brightness region. ### Claim 21 (depends on 1) A non-transitory computer-readable information storage medium storing a program for causing a computer to implement the method of claim 1. ### Claim 22 (depends on 6) A non-transitory computer-readable information storage medium storing a program for causing a computer to implement the method of claim 6.