WO2022226886A1 - Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori - Google Patents

Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori Download PDF

Info

Publication number
WO2022226886A1
WO2022226886A1 PCT/CN2021/090956 CN2021090956W WO2022226886A1 WO 2022226886 A1 WO2022226886 A1 WO 2022226886A1 CN 2021090956 W CN2021090956 W CN 2021090956W WO 2022226886 A1 WO2022226886 A1 WO 2022226886A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
transform domain
network
denoising
domain
Prior art date
Application number
PCT/CN2021/090956
Other languages
English (en)
Chinese (zh)
Inventor
李彦明
郑海荣
刘新
万丽雯
胡战利
周瑾洁
Original Assignee
深圳高性能医疗器械国家研究院有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳高性能医疗器械国家研究院有限公司 filed Critical 深圳高性能医疗器械国家研究院有限公司
Priority to PCT/CN2021/090956 priority Critical patent/WO2022226886A1/fr
Publication of WO2022226886A1 publication Critical patent/WO2022226886A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/60—Image enhancement or restoration using machine learning, e.g. neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • the present invention relates to the technical field of medical image processing, and more particularly, to an image processing method based on transform domain denoising automatic encoder as a priori.
  • X-ray computed tomography is used for diagnosis and intervention in hospitals and clinics.
  • X-ray CT may pose a potential risk of cancer or genetic disease due to exposure to radiation.
  • X-CT medical imaging images have the advantages of high density and resolution of tissue structures and little damage to the human body, and are very important for the study of pathology and anatomy.
  • the phenomenon of blurred images or indistinct borders will occur, resulting in low readability of X-CT medical image images, and doctors cannot make accurate diagnosis. Therefore it is necessary to reduce the X-ray dose.
  • X-ray dose is a key indicator in X-CT medical imaging images. The higher the X-ray dose, the clearer the image. However, with the increase in the dose of X-rays, the harm to the human body continues to increase. At present, the equipment in many hospitals has reached the minimum dose requirements, but the minimum dose of CT will be accompanied by low quality and noise. Obtaining high-quality CT images under the condition of low dose (minimum harm to human body) has important scientific significance and broad application prospects for the field of medical diagnosis.
  • the main defects of the existing CT image processing are: considering the potential risk of emitting X-rays to patients, low-dose CT is a commonly used diagnostic evidence in clinical medicine, but low-dose imaging agents in CT imaging will lead to The reconstructed images generate a lot of quantum noise and blurred morphological features; in existing deep learning-based image reconstruction schemes, the dataset used is a pair of low- and high-dose CT image pairs, but in real life, clean The one-to-one correspondence of CT images is rare.
  • low-dose images are generated by applying Poisson noise to each detector element simulating a normal dose sinusoid with a blank scanning flux, which is complicated and inefficient.
  • the purpose of the present invention is to overcome the above-mentioned defects of the prior art, and to provide an image processing method based on transform domain denoising auto-encoder as a priori, which is a new method for denoising low-dose images using prior information of unsupervised learning.
  • Technical solutions are to overcome the above-mentioned defects of the prior art, and to provide an image processing method based on transform domain denoising auto-encoder as a priori, which is a new method for denoising low-dose images using prior information of unsupervised learning.
  • an image processing method based on transform domain denoising auto-encoder as a priori includes the following steps:
  • Step S1 Construct a multi-channel tensor space with multi-scale and multi-view characteristics using the original image and multi-channel transformation features, and construct a training data set;
  • Step S2 train a denoising auto-encoder network based on the training data set to combine the image transform domain with the original pixel domain, obtain an image in the transform domain, and use the image in the transform domain to learn the multi-channel tensor space. the prior information;
  • step S3 the prior information learned from the multi-channel tensor space is introduced into the iterative process of processing the image restoration problem to solve, and an optimized denoising auto-encoder network is obtained.
  • an image processing method includes: transforming the image to be processed to obtain a transform domain image;
  • the to-be-processed image and the image transform domain are combined, input to the optimized denoising auto-encoder network obtained according to the present invention, and the reconstructed image is output.
  • the present invention has the advantage that the transform domain-based denoising auto-encoder is provided as a priori image processing method, and the core idea is to enhance the classical de-noising auto-encoder (DAE) by transforming the domain,
  • DAE classical de-noising auto-encoder
  • FIG. 1 is a flowchart of an image processing method based on transform domain denoising auto-encoder as a priori according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of the overall process of an image processing method based on transform domain denoising auto-encoder as a priori according to an embodiment of the present invention
  • FIG. 3 is a flow chart of network learning based on transform domain denoising auto-encoder as a priori according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram of experimental results according to an embodiment of the present invention.
  • the image processing method based on transform domain denoising autoencoder as a priori provided by the present invention can be applied to various types of image reconstruction such as magnetic resonance imaging, computed tomography, positron emission computed tomography, etc. block, demosaicing, etc.
  • image reconstruction such as magnetic resonance imaging, computed tomography, positron emission computed tomography, etc. block, demosaicing, etc.
  • CT image denoising takes CT image denoising as an example.
  • the present invention proposes a CT denoising algorithm based on the transform domain denoising automatic encoder as a priori (TDAEP-CT).
  • TDAEP-CT transform domain denoising automatic encoder
  • DAE classic denoising autoencoder
  • the method includes: using non-orthogonal wavelet coefficients to form a multi-channel feature image (such as a 4-channel feature image); obtaining a multi-channel image by stacking the original image in the pixel domain and the multi-channel feature image in the wavelet domain.
  • quantile such as a 5-channel tensor
  • DAE transformed DAE
  • TDAE transformed DAE
  • auxiliary variable techniques Incorporate it into the iterative recovery process.
  • the provided image processing method based on transform domain denoising autoencoder as a priori includes the following steps.
  • Step S110 register the CT images that do not correspond one to one, generate a multi-channel CT tensor, and construct a training data set.
  • this step includes: first, normalizing the CT images that do not correspond to one-to-one so as to keep the size consistent in the training phase, and then performing wavelet transformation on the CT images (for example, using 1
  • a 5-dimensional CT image tensor is formed by stacking four wavelet images with an original image.
  • the one-to-one non-corresponding CT images are shown in Figure 2(a)
  • the process of wavelet transform of CT images is shown in Figure 2(b)
  • the formation of 5-dimensional CT image tensors is shown in Figure 2(c) Show.
  • this embodiment uses a wavelet transform (WT) to generate the change domain.
  • WT wavelet transform
  • Wavelet transform can effectively analyze image features, especially image details.
  • the pseudo-Gibbs phenomenon occurs near the discontinuity of the extracted signal. It causes alternating undershoots and overshoots near singularities of the reconstructed signal and produces blocky artifacts in the processed image.
  • TIWT Translation Invariant Wavelet Transform or Cyclic Spinning
  • TIWT computes the inner product between all (circular) translated versions of the image and wavelet basis functions. Restoration can be achieved sequentially by thresholding and averaging operators. Using TIWT can avoid the pseudo-Gibbs phenomenon in the denoising process, and obtain better gain than DWT (Discrete Wavelet Transform) in removing noise and restoring the reduced high frequency components.
  • DWT Discrete Wavelet Transform
  • the overcomplete wavelet transform consists of N orthogonal wavelet transforms, each of which consists of a cyclic shift of a wavelet basis function.
  • Will is the basic orthogonal wavelet transform matrix, represents the possible wavelet transform matrix, applying the circular image shift to the basis functions , the TIWT matrix and its inverse process are expressed as:
  • W T W ⁇ I and W is not orthogonal.
  • the original image is decomposed into 4 sub-band images: approximate part LL and detail including horizontal component HL, vertical component LH and diagonal component HH (each having 1/4 the size of the original image) part.
  • the low frequency component is the subband LL that contains most of the information of the original image.
  • the subbands denoted HL, LH and HH contain the finest scale detail wavelet coefficients, corresponding to the higher frequency detail information of the original image. It should be noted that after 2D-TIWT decomposition, each subband image always has almost the same size as the original input image.
  • a 2D inverse translation-invariant wavelet transform consisting of four subbands can completely reconstruct the original image.
  • image priors with multi-scale and multi-view characteristics are learned by TIWT.
  • the faceted data obtained from the transform domain provides more contour prior information, which is of great help in dealing with restoration tasks.
  • the embodiment of the present invention constructs a multi-faceted data composed of elements in the wavelet domain and the pixel domain, and forms a tensor as the network input.
  • Figure 2(c) depicts the formation of a 5-channel tensor in the transform domain.
  • the final training data is Among them, the former component Ix is the original image, and the latter component Wx represents the combination of four subband images.
  • Step S120 using the training data set to train the denoising auto-encoder to learn the prior in the transform domain.
  • the network design process is illustrated by taking CT Image-based Enhanced Classical Denoising Autoencoder (TDAEP-CT) as an example.
  • TDAEP-CT CT Image-based Enhanced Classical Denoising Autoencoder
  • Equation (3) the optimal DAE reconstruction function at each point x is given by a convolution of the density function p, that is, the weighted average of each point in the neighborhood x.
  • the autoencoder error is proportional to the log-likelihood gradient of the smoothed density, that is:
  • DAEP adopts the transfer characteristics of prior information and uses the magnitude of this mean shift vector as the negative log-likelihood of the image prior, which is expressed as:
  • DMSP Deep Mean Shift Prior
  • a high-dimensional embedding network can also be employed, which precedes the derivation and applies the learned prior to single-channel MRI through variable enhancement techniques reconstruction.
  • the TDAEP-CT provided by the present invention mainly includes two processes: learning the prior information in the 5-channel tensor space instead of the original CT pixel space; the prior information learned from the 5-channel tensor space is introduced into the processing of CT image restoration during the iterative process of the problem.
  • TDAE TDAE network
  • TDAEP prior a TDAE network is trained from data pairs consisting of 5-channel tensors and their noisy versions.
  • TDAEP prior is defined as:
  • x is the original image
  • a 5-channel tensor in the transform domain is represented as Among them, the former component Ix is the original image, and the latter component Wx represents the combination of four subband images.
  • DAE is Its output is in represents the two-norm.
  • the biggest innovation of the present invention is to learn the prior information in the transform domain and apply it to the image restoration task.
  • the image restoration task the image wavelet domain is combined with the original pixel domain to obtain the image in the transformed domain, and it is used to drive the network to extract image priors.
  • TDAEP is superior to DAEP in image feature extraction.
  • Using the image transform domain can enhance the image restoration process.
  • the biggest innovation of this work is to learn the prior information in the transform domain and apply it to the IR (image reconstruction) task.
  • x is the original image
  • M is the degradation factor/operator
  • y is the generated image after degradation
  • n is the additive noise
  • the parameter ⁇ is the control data fidelity term and A compromise between regularization terms.
  • the regular terms extracted from the wavelet domain are as follows:
  • the present invention jointly learns them as tensors by superposition, accompanied by a loss function with lower penalty. Better learning ability helps the network to effectively extract redundant feature information and generate more compact representations.
  • the multi-scale and multi-view properties of the transform domain are achieved by adding artificial noise to the pixel and wavelet domains simultaneously. They complement each other to obtain higher quality prior information.
  • the network is trained and used by the following two equations:
  • the network architecture design of the present invention can use various types of end-to-end convolutional neural networks, such as ResNet, densente and DualPathNet.
  • ResNet introduces a fast connection scheme so that the last residual block flows directly into the next one. Therefore, it improves information flow and avoids vanishing gradients.
  • ResNet Due to the good performance of ResNet in VDSR, EDSR and SRGAN, the architecture of TDAE network uses ResNet as a building block in the present invention.
  • both the input and output of the TDAE network are 5-dimensional tensors.
  • the main body of the network includes five components, each of which is composed of "CONV+BN+ReLU", “CONV+BN” and “ReLU” components.
  • CONV convolutional layers
  • BN convolutional layers
  • ReLU rectified linear units used to accelerate network learning, respectively.
  • the number of core filters in each convolutional layer is set to 320, except that the number of filters in the last layer is 5.
  • the kernel size of each convolutional layer is set to 3 ⁇ 3. It can be seen that its structure is similar to DnCNN (Denoising Convolutional Neural Network) except for network input and output and additional ResNet blocks. It should be noted that in TDAE, a more complex network can be used to ensure more efficient learning ability.
  • step S130 an optimized denoising auto-encoder network is obtained by iterative solution.
  • a proximal gradient method is employed to handle the nonlinearity of the network and the resulting model equations.
  • the model can be approximated by standard least squares minimization, expressed as:
  • Equation (16) is a standard LS (least squares) problem, which can be solved by computing the gradient as follows:
  • R represents the averaging operator used on the first channel image and intermediate ITIWT results. has been learned during the network training phase.
  • Figure 3 is a network flow chart for TDAEP learning, in which the input is a 5-channel image, plus artificial Gaussian noise; the middle part shows a 20-layer network, consisting of 5 residual “blocks", 1 "CONV+ReLU” ", 3 "CONV+BN+ReLU” and 1 "CONV", the specific structure of the "block” refers to the upper part of Figure 3.
  • the present invention extracts the prior in the transform domain, that is, jointly extracts the prior of the damaged object in the pixel domain and the intermediate wavelet domain, rather than in the pixel domain or the wavelet domain, respectively, which is constructed from the original image and multi-channel transform features.
  • TSWT translation-invariant wavelet transform
  • noise and high-frequency components can be efficiently optimized.
  • different noise weighting strategies are adopted in the network design process, which makes the design process more robust and stable for different restoration tasks. This strategy is beneficial to avoid falling into local minima and make the iterative process more stable.
  • alternating optimization and approximate gradient descent techniques are employed to solve the non-convex image restoration minimization problem.
  • the present invention may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.
  • a computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disk read only memory
  • DVD digital versatile disk
  • memory sticks floppy disks
  • mechanically coded devices such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
  • the computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • the computer program instructions for carrying out the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages.
  • Source or object code written in any combination including object-oriented programming languages, such as Smalltalk, C++, Python, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages.
  • the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect).
  • LAN local area network
  • WAN wide area network
  • custom electronic circuits such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs)
  • FPGAs field programmable gate arrays
  • PDAs programmable logic arrays
  • Computer readable program instructions are executed to implement various aspects of the present invention.
  • These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium storing the instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.
  • Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions.
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are all equivalent.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)

Abstract

L'invention divulgue un procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori. Le procédé consiste : à utiliser une image originale et des caractéristiques de transformation multicanal pour construire un espace de tenseur multicanal présentant des caractéristiques multi-échelles et multi-vues et à construire un ensemble de données d'apprentissage ; à former un réseau d'auto-encodeurs de débruitage sur la base de l'ensemble de données d'apprentissage de sorte à combiner un domaine de transformation d'image avec un domaine de pixel d'origine pour obtenir une image dans le domaine de transformation et à apprendre des informations a priori dans l'espace de tenseur multicanal en utilisant l'image dans le domaine de transformation ; et à introduire les informations a priori apprises à partir de l'espace de tenseur multicanal dans un processus d'itération pour traiter des problèmes de restauration d'image pour effectuer une résolution de sorte à obtenir un réseau d'auto-encodeurs de débruitage optimisé. À l'aide d'une image reconstruite obtenue dans la présente invention, la qualité de l'image est améliorée tout en conservant davantage de détails de texture, et les exigences de diagnostic sont mieux satisfaites.
PCT/CN2021/090956 2021-04-29 2021-04-29 Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori WO2022226886A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2021/090956 WO2022226886A1 (fr) 2021-04-29 2021-04-29 Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2021/090956 WO2022226886A1 (fr) 2021-04-29 2021-04-29 Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori

Publications (1)

Publication Number Publication Date
WO2022226886A1 true WO2022226886A1 (fr) 2022-11-03

Family

ID=83846603

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/090956 WO2022226886A1 (fr) 2021-04-29 2021-04-29 Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori

Country Status (1)

Country Link
WO (1) WO2022226886A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117011673A (zh) * 2023-10-07 2023-11-07 之江实验室 基于噪声扩散学习的电阻抗层析成像图像重建方法和装置
CN117495714A (zh) * 2024-01-03 2024-02-02 华侨大学 基于扩散生成先验的人脸图像复原方法、装置及可读介质
CN117689761A (zh) * 2024-02-02 2024-03-12 北京航空航天大学 基于扩散模型的即插即用磁粒子成像重建方法、系统
CN118521670A (zh) * 2024-07-19 2024-08-20 腾讯科技（深圳）有限公司 图像伪影去除方法、图像伪影去除模型的训练方法及装置

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110223255A (zh) * 2019-06-11 2019-09-10 太原科技大学 一种用于低剂量ct图像去噪的浅层残差编解码递归网络
CN110246094A (zh) * 2019-05-13 2019-09-17 南昌大学 一种用于彩色图像超分辨率重建的6维嵌入的去噪自编码先验信息算法
CN111047524A (zh) * 2019-11-13 2020-04-21 浙江工业大学 基于深度卷积神经网络的低剂量ct肺部图像的去噪方法
CN112330682A (zh) * 2020-11-09 2021-02-05 重庆邮电大学 一种基于深度卷积神经网络的工业ct图像分割方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110246094A (zh) * 2019-05-13 2019-09-17 南昌大学 一种用于彩色图像超分辨率重建的6维嵌入的去噪自编码先验信息算法
CN110223255A (zh) * 2019-06-11 2019-09-10 太原科技大学 一种用于低剂量ct图像去噪的浅层残差编解码递归网络
CN111047524A (zh) * 2019-11-13 2020-04-21 浙江工业大学 基于深度卷积神经网络的低剂量ct肺部图像的去噪方法
CN112330682A (zh) * 2020-11-09 2021-02-05 重庆邮电大学 一种基于深度卷积神经网络的工业ct图像分割方法

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117011673A (zh) * 2023-10-07 2023-11-07 之江实验室 基于噪声扩散学习的电阻抗层析成像图像重建方法和装置
CN117011673B (zh) * 2023-10-07 2024-03-26 之江实验室 基于噪声扩散学习的电阻抗层析成像图像重建方法和装置
CN117495714A (zh) * 2024-01-03 2024-02-02 华侨大学 基于扩散生成先验的人脸图像复原方法、装置及可读介质
CN117495714B (zh) * 2024-01-03 2024-04-12 华侨大学 基于扩散生成先验的人脸图像复原方法、装置及可读介质
CN117689761A (zh) * 2024-02-02 2024-03-12 北京航空航天大学 基于扩散模型的即插即用磁粒子成像重建方法、系统
CN117689761B (zh) * 2024-02-02 2024-04-26 北京航空航天大学 基于扩散模型的即插即用磁粒子成像重建方法、系统
CN118521670A (zh) * 2024-07-19 2024-08-20 腾讯科技（深圳）有限公司 图像伪影去除方法、图像伪影去除模型的训练方法及装置

Similar Documents

Publication Publication Date Title
WO2022226886A1 (fr) Procédé de traitement d'image basé sur un auto-encodeur de débruitage de domaine de transformée a priori
Ran et al. Denoising of 3D magnetic resonance images using a residual encoder–decoder Wasserstein generative adversarial network
Li et al. SACNN: Self-attention convolutional neural network for low-dose CT denoising with self-supervised perceptual loss network
Diwakar et al. CT image denoising using NLM and correlation‐based wavelet packet thresholding
CN104182954B (zh) 一种实时的多模态医学图像融合方法
Dakua et al. Patient oriented graph-based image segmentation
WO2021168920A1 (fr) Procédé et système d'amélioration d'image à faible dose sur la base de multiples niveaux de dose, dispositif informatique, et support de stockage
Yang et al. Super-resolution of medical image using representation learning
CN115018728A (zh) 基于多尺度变换和卷积稀疏表示的图像融合方法及系统
Zhu et al. STEDNet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose CT
Yin et al. Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensemble
Zhao et al. Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising
Manimala et al. Sparse MR image reconstruction considering Rician noise models: A CNN approach
WO2023279316A1 (fr) Procédé de reconstruction tep basé sur un réseau de mise en correspondance de scores de débruitage
Liu et al. DFSNE-Net: Deviant feature sensitive noise estimate network for low-dose CT denoising
Wu et al. Masked joint bilateral filtering via deep image prior for digital X-ray image denoising
CN112991220B (zh) 一种基于多重约束的卷积神经网络校正图像伪影的方法
CN113129296B (zh) 基于变换域下去噪自动编码器作为先验的图像处理方法
Liu et al. Windowed variation kernel Wiener filter model for image denoising with edge preservation
Li et al. Dual-domain fusion deep convolutional neural network for low-dose CT denoising
Jiang et al. GDAFormer: Gradient-guided Dual Attention Transformer for Low-Dose CT image denoising
Hein et al. PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models
Mahmoud et al. Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising.
Kang et al. Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder Network
Li et al. Adaptive weighted total variation expansion and Gaussian curvature guided low-dose CT image denoising network

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21938367

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 29-02-2024)