JP-2026076141-A - Image processing method, program, and image processing apparatus

Abstract

[Challenge] To improve image quality. [Solution] The image processing method according to the embodiment obtains a diffusion-based probabilistic model trained to perform denoising over a plurality of denoising steps using a target image and a conditional image, repeatedly performs the denoising steps based on the probabilistic model and a first seed image function to generate an intermediate image from the input image, and repeatedly performs the denoising steps based on the probabilistic model and a second seed image function to generate a denoised output image from the intermediate image. [Selection Diagram] Figure 7

Inventors

• ツ チェン リー
• シー チェン
• リヤン ツァイ

Assignees

• キヤノンメディカルシステムズ株式会社

Dates

Publication Date

20260511

Application Date

20251023

Priority Date

20241023

Claims (20)

1. Using the target image and conditional image, we obtain a diffusion-based stochastic model trained to perform denoising across multiple denoising steps. Based on the aforementioned probabilistic model and the first seed image function, the denoising step is repeatedly performed to generate an intermediate image from the input image. An image processing method that repeatedly performs a denoising step based on the aforementioned probability model and a second seed image function to generate a denoised output image from the intermediate image.
2. The image processing method according to claim 1, wherein the second seed image function is based on the image characteristics of the output image.
3. The image processing method according to claim 2, wherein the aforementioned image characteristic is image resolution.
4. The image processing method according to claim 2, wherein the aforementioned image characteristic is the image noise level.
5. The image processing method according to claim 1, wherein the second seed image function includes a unit Gaussian noise image to which a scalar multiplier has been applied.
6. The image processing method according to claim 1, wherein the second seed image function includes a unit Gaussian noise image to which a low-pass filter has been applied.
7. The image processing method according to claim 1, wherein the number of repetitions of the noise reduction step performed when generating the intermediate image from the input image, or the number of repetitions of the noise reduction step performed when generating the output image from the intermediate image, is set based on the image characteristics of the output image.
8. On the computer, Using the target image and conditional image, we obtain a diffusion-based stochastic model trained to perform denoising across multiple denoising steps. Based on the aforementioned probabilistic model and the first seed image function, the denoising step is repeatedly performed to generate an intermediate image from the input image. The process involves repeatedly executing a denoising step based on the aforementioned probability model and second seed image function to generate a denoised output image from the intermediate image. program.
9. The program according to claim 8, wherein the second seed image function is based on the image characteristics of the output image.
10. The program according to claim 9, wherein the aforementioned image characteristic is image resolution.
11. The program according to claim 9, wherein the aforementioned image characteristic is the image noise level.
12. The program according to claim 8, wherein the second seed image function includes applying a scalar multiplier to a unit Gaussian noise image.
13. The program according to claim 8, wherein the second seed image function includes a unit Gaussian noise image to which a low-pass filter has been applied.
14. The program according to claim 8, wherein the number of repetitions of the noise reduction step performed when generating the intermediate image from the input image, or the number of repetitions of the noise reduction step performed when generating the output image from the intermediate image, is set based on the image characteristics of the output image.
15. Using the target image and conditional image, we obtain a diffusion-based stochastic model trained to perform denoising across multiple denoising steps. Based on the aforementioned probabilistic model and the first seed image function, the denoising step is repeatedly performed to generate an intermediate image from the input image. An image processing apparatus comprising a processing circuit that repeatedly performs a denoising step based on the aforementioned probability model and a second seed image function to generate a denoised output image from the intermediate image.
16. The image processing apparatus according to claim 15, wherein the second seed image function is based on the image characteristics of the output image.
17. The image processing apparatus according to claim 16, wherein the aforementioned image characteristic is image resolution.
18. The image processing apparatus according to claim 16, wherein the aforementioned image characteristic is the image noise level.
19. The image processing apparatus according to claim 15, wherein the second seed image function includes a unit Gaussian noise image to which a multiplier has been applied.
20. The image processing apparatus according to claim 15, wherein the second seed image function includes a unit Gaussian noise image to which a low-pass filter has been applied.

Description

Embodiments disclosed herein and in the drawings relate to image processing methods, programs, and image processing apparatus. Deep learning-based image restoration models, such as Denoising Diffusion Probabilistic Models (DDPMs), typically use a series of steps to modify the input image to the desired degree. However, the desirable properties of the reconstructed image may vary based on numerous controllable and uncontrollable factors. Furthermore, the image reconstruction process utilized by the deep learning model may influence the properties of the reconstructed image. U.S. Patent No. 2024/0135611U.S. Patent No. 2024/0161864U.S. Patent No. 2015/0093010 Figure 1 shows a series of noise-reduced images according to the embodiment.Figure 2A shows a noise-reduced image according to the embodiment.Figure 2B shows a denoised image using the scaling seed image function according to the embodiment.Figure 2C shows a denoised image using the low-pass filter seed image function according to the embodiment.Figure 3A shows a denoised image using the scaling seed image function according to the embodiment.Figure 3B shows a denoised image using the scaling seed image function according to the embodiment.Figure 3C shows a denoised image using the scaling seed image function according to the embodiment.Figure 4A shows a denoised image using the low-pass filter seed image function according to the embodiment.Figure 4B shows a denoised image using the low-pass filter seed image function according to the embodiment.Figure 4C shows a denoised image using the low-pass filter seed image function according to the embodiment.Figure 5 shows the workflow for image noise reduction according to the embodiment.Figure 6 shows the workflow for image noise reduction according to the embodiment.Figure 7 shows a method for removing image noise according to an embodiment.Figure 8 is a schematic diagram of a hardware system for carrying out the method according to the embodiment.Figure 9 is a schematic diagram of an imaging system according to an embodiment. The following describes in detail the image processing method, program, and image processing apparatus with reference to the drawings. When referring to drawings, the same reference number indicates the same or corresponding part across multiple drawings. As used herein, the term “plural” is defined as two or more. As used herein, the term “another” is defined as at least two or more. As used herein, the terms “including” and/or “having” are defined as comprising (i.e., open language). Throughout this specification, any reference to “one embodiment,” “a particular embodiment,” “embodiment,” “implementation,” “implementation,” “example,” or similar terms means that a particular feature, structure, or characteristic described in relation to this embodiment is included in at least one embodiment of this disclosure. Therefore, not all such phrases or occurrences throughout this specification necessarily refer to the same embodiment. Furthermore, a particular feature, structure, or characteristic may be combined in any suitable manner without being limited to one or more embodiments. In one embodiment, this disclosure relates to a system and method for image restoration using a deep learning-based model. Image restoration techniques may include, but are not limited to, denoising, deblurring, resolution enhancement (e.g., super-resolution imaging), and image/signal reconstruction (e.g., compressed sensing). Each of these techniques can be used independently or in combination to improve the visibility of features in an image. Image restoration has important applications in medical imaging modalities such as computed tomography (CT) scanning and magnetic resonance imaging (MRI), which are often affected by noise from physical interactions within the imaging system. It will be understood that the systems and methods described herein are not limited to medical imaging applications and can be used in a variety of imaging types and techniques. In particular, the methods of this disclosure may be useful for processing image data of any volume (e.g., a series of images or image slices) acquired over a spatial or temporal span. Generative deep learning-based models can be used to reduce noise and similar artifacts in acquired images and generate restored images of higher quality than the acquired images. In one embodiment, the generative model can be used to denoise an image by transforming a first data distribution (noisy image data) into a second data distribution (restored image data). In one embodiment, the generative model may be a denoising diffusion probabilistic model (DDPM). The DDPM is described herein as an exemplary example of a class of generative models, and it may be understood that other types of probabilistic models, particularly diffusion-based probabilistic models for image restoration, are also suitable for the methods of this disclosure. DDPM can be used to denoise an image in a s