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Image processing method, image processing apparatus, program, image processing system, and manufacturing method of learnt model

An image processing and image technology, applied in image data processing, image enhancement, image analysis and other directions, can solve problems such as increasing data volume

Pending Publication Date: 2020-09-15
CANON KK
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This method needs to store the learning results for each noise volume, and may increase the amount of data

Method used

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  • Image processing method, image processing apparatus, program, image processing system, and manufacturing method of learnt model
  • Image processing method, image processing apparatus, program, image processing system, and manufacturing method of learnt model
  • Image processing method, image processing apparatus, program, image processing system, and manufacturing method of learnt model

Examples

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no. 1 example

[0029] A description will now be given of an image processing system according to a first embodiment of the present invention. In the first embodiment, the neural network performs the recognition task of detecting human regions in images (segmentation of people). However, the invention is not limited to this embodiment and is similarly applicable to other recognition or regression tasks.

[0030] figure 2 is a block diagram of the image processing system 100 according to this embodiment. image 3 is an external view of the image processing system 100 . The image processing system 100 includes a learning device 101 , an imaging device (image pickup device) 102 and a network 103 . The learning device 101 includes a memory 111, an acquirer (acquiring unit) 112, a detector (detecting unit) 113, and an updater (updating unit) 114, and learns weights of a neural network that detects human regions. Details of this learning will be described later. The weight information learned...

no. 2 example

[0048] A description will be given of an image processing system according to a second embodiment of the present invention. In this embodiment, the neural network performs a regression task (deblurring) for correcting aberrations and blurring due to diffraction of captured images. However, the invention is not limited to this embodiment and can be applied to other recognition or regression tasks.

[0049] Figure 7 is a block diagram of the image processing system 300 according to this embodiment. Figure 8 is an external view of the image processing system 300 . The image processing system 300 includes a learning device 301 , an imaging device 302 , an image estimation device 303 , a display device 304 , a recording medium 305 , an output device 306 , and a network 307 .

[0050] The learning device 301 includes a memory 301a, an acquirer 301b, a generator 301c, and an updater 301d. The imaging device 302 includes an optical system 302a and an image sensor 302b. The imag...

no. 3 example

[0069] A description will now be given of an image processing system according to a third embodiment of the present invention. The image processing system according to this embodiment is different from that of each of the first and second embodiments in that it has a processing device (computer) that transmits a captured image as an image processing target to and receive the processed output image from the image estimation device.

[0070] Figure 13 is a block diagram of the image processing system 600 according to this embodiment. The image processing system 600 includes a learning device 601 , an imaging device 602 , an image estimation device 603 , and a processing device (computer) 604 . The learning device 601 and the image estimating device 603 are, for example, servers. The computer 604 is, for example, a user terminal such as a personal computer and a smartphone. The computer 604 is connected to the image estimation device 603 via a network 605 . The image estima...

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Abstract

An image processing method includes acquiring (S201, S202, S401 to S404) input data (201, 511) having an input image (402) and a noise map (405) representing a noise amount in the input image based onan optical black area (403) corresponding to the input image, and inputting (S203, S405) the input data into a neural network to execute a task of a recognition or regression.

Description

technical field [0001] The present invention relates to image processing methods that can improve the robustness against noise in neural networks. Background technique [0002] Japanese Patent Laid-Open No. ("JP") 2016-110232 discloses a method for determining the position of a recognition target in an image with high accuracy using a neural network. [0003] However, the method disclosed in JP 2016-110232 reduces determination accuracy when an image has a low S / N ratio. Since the noise in the image depends on the ISO sensitivity and the performance of the image sensor during imaging, images with noise of various intensities can be input to the neural network. A low S / N ratio will reduce recognition accuracy because object feature quantities cannot be exclusively extracted due to noise influence. [0004] There are two conceivable ways to improve the robustness of neural networks against noise. The first method is to include images with various amounts of noise in the lea...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N5/357H04N5/361H04N9/04G06T5/00G06N3/08G06V10/30
CPCG06N3/08G06T2207/20081G06T2207/20084H04N25/63H04N25/11G06T5/73G06T5/70H04N25/674G06T7/11G06V10/30G06V10/82H04N25/671G06T3/4046G06F18/2148G06F18/217
Inventor 日浅法人
Owner CANON KK
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