KR20210055583A - 이미지 노이즈 제거를 위한 적응형 디포머블 커널 예측 네트워크 - Google Patents

이미지 노이즈 제거를 위한 적응형 디포머블 커널 예측 네트워크 Download PDF

Info

Publication number
KR20210055583A
KR20210055583A KR1020200124306A KR20200124306A KR20210055583A KR 20210055583 A KR20210055583 A KR 20210055583A KR 1020200124306 A KR1020200124306 A KR 1020200124306A KR 20200124306 A KR20200124306 A KR 20200124306A KR 20210055583 A KR20210055583 A KR 20210055583A
Authority
KR
South Korea
Prior art keywords
graphics
pixel
memory
processor
data
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
KR1020200124306A
Other languages
English (en)
Korean (ko)
Inventor
안방 야오
밍 루
위카이 왕
샤오밍 첸
준지에 후앙
타오 려
유안케 루오
위 양
펭 첸
지밍 왕
지퀴아오 정
샨동 왕
Original Assignee
인텔 코포레이션
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 인텔 코포레이션 filed Critical 인텔 코포레이션
Publication of KR20210055583A publication Critical patent/KR20210055583A/ko
Pending legal-status Critical Current

Links

Images

Classifications

    • G06T5/002—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/20—Processor architectures; Processor configuration, e.g. pipelining
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
    • G06F9/38—Concurrent instruction execution, e.g. pipeline or look ahead
    • G06F9/3802—Instruction prefetching
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
    • G06F9/38—Concurrent instruction execution, e.g. pipeline or look ahead
    • G06F9/3802—Instruction prefetching
    • G06F9/3804—Instruction prefetching for branches, e.g. hedging, branch folding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
    • G06F9/38—Concurrent instruction execution, e.g. pipeline or look ahead
    • G06F9/3885—Concurrent instruction execution, e.g. pipeline or look ahead using a plurality of independent parallel functional units
    • G06F9/3887—Concurrent instruction execution, e.g. pipeline or look ahead using a plurality of independent parallel functional units controlled by a single instruction for multiple data lanes [SIMD]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0454—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4046—Scaling of whole images or parts thereof, e.g. expanding or contracting using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Neurology (AREA)
  • Image Processing (AREA)
  • Image Generation (AREA)
  • Facsimile Image Signal Circuits (AREA)
KR1020200124306A 2019-11-07 2020-09-24 이미지 노이즈 제거를 위한 적응형 디포머블 커널 예측 네트워크 Pending KR20210055583A (ko)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201911081492.0 2019-11-07
CN201911081492.0A CN112785486A (zh) 2019-11-07 2019-11-07 用于图像去噪声的自适应可变形核预测网络

Publications (1)

Publication Number Publication Date
KR20210055583A true KR20210055583A (ko) 2021-05-17

Family

ID=75584071

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020200124306A Pending KR20210055583A (ko) 2019-11-07 2020-09-24 이미지 노이즈 제거를 위한 적응형 디포머블 커널 예측 네트워크

Country Status (5)

Country Link
US (2) US11869171B2 (https=)
JP (1) JP2021077343A (https=)
KR (1) KR20210055583A (https=)
CN (1) CN112785486A (https=)
DE (1) DE102020129251A1 (https=)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11869171B2 (en) 2019-11-07 2024-01-09 Intel Corporation Adaptive deformable kernel prediction network for image de-noising
US12211172B2 (en) 2021-10-07 2025-01-28 Samsung Electronics Co., Ltd. Display device and operating method of the same

Families Citing this family (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021093958A1 (en) * 2019-11-14 2021-05-20 Huawei Technologies Co., Ltd. Spatially adaptive image filtering
US11418455B2 (en) 2020-08-31 2022-08-16 Micron Technology, Inc. Transparent packet splitting and recombining
US11539623B2 (en) 2020-08-31 2022-12-27 Micron Technology, Inc. Single field for encoding multiple elements
US11412075B2 (en) 2020-08-31 2022-08-09 Micron Technology, Inc. Multiple protocol header processing
US11296995B2 (en) 2020-08-31 2022-04-05 Micron Technology, Inc. Reduced sized encoding of packet length field
US11360920B2 (en) 2020-08-31 2022-06-14 Micron Technology, Inc. Mapping high-speed, point-to-point interface channels to packet virtual channels
US11871145B2 (en) * 2021-04-06 2024-01-09 Adobe Inc. Optimization of adaptive convolutions for video frame interpolation
US12283028B2 (en) * 2021-06-02 2025-04-22 Nvidia Corporation Spatio-temporal noise masks for image processing
CN113516235B (zh) * 2021-07-13 2024-10-18 南京大学 一种可变形卷积加速器和可变形卷积加速方法
CN113744156B (zh) * 2021-09-06 2022-08-19 中南大学 一种基于可变形卷积神经网络的图像去噪方法
KR102785795B1 (ko) * 2021-09-13 2025-03-26 삼성전자주식회사 대조도 조절 방법 및 이를 이용하는 장치
CN113689359B (zh) * 2021-09-23 2024-05-14 上海联影医疗科技股份有限公司 一种图像伪影去除模型及其训练方法和系统
IT202100026552A1 (it) * 2021-10-18 2023-04-18 Durst Group Ag "Metodo e prodotto per la sintesi di dati di stampa e per la fornitura degli stessi ad una stampante"
CN113963009B (zh) * 2021-12-22 2022-03-18 中科视语(北京)科技有限公司 基于可形变划块的局部自注意力的图像处理方法和系统
US12462337B2 (en) * 2022-02-25 2025-11-04 Arm Limited System, devices and/or processes for processing image pixel values
CN114998964B (zh) * 2022-06-02 2023-04-18 天津道简智创信息科技有限公司 一种新型证照质量检测方法
GB2620920B (en) * 2022-07-21 2024-09-25 Advanced Risc Mach Ltd System, devices and/or processes for application of kernel coefficients
US12008728B2 (en) 2022-08-31 2024-06-11 Qualcomm Incorporated Apparatuses and methods for processing single instruction for image transformation from non-integral locations
CN115661784B (zh) * 2022-10-12 2023-08-22 北京惠朗时代科技有限公司 一种面向智慧交通的交通标志图像大数据识别方法与系统
CN116363480B (zh) * 2023-03-20 2026-03-31 南京大学 一种用于图像像素处理网络的计算装置和方法
CN119313587B (zh) * 2024-12-18 2025-03-28 浙江大华技术股份有限公司 基于块匹配的图像降噪方法、设备及存储介质

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7873812B1 (en) 2004-04-05 2011-01-18 Tibet MIMAR Method and system for efficient matrix multiplication in a SIMD processor architecture
US7747070B2 (en) * 2005-08-31 2010-06-29 Microsoft Corporation Training convolutional neural networks on graphics processing units
US10223333B2 (en) 2014-08-29 2019-03-05 Nvidia Corporation Performing multi-convolution operations in a parallel processing system
US10402700B2 (en) * 2016-01-25 2019-09-03 Deepmind Technologies Limited Generating images using neural networks
US10528864B2 (en) 2016-08-11 2020-01-07 Nvidia Corporation Sparse convolutional neural network accelerator
US10891538B2 (en) 2016-08-11 2021-01-12 Nvidia Corporation Sparse convolutional neural network accelerator
US11531852B2 (en) * 2016-11-28 2022-12-20 D-Wave Systems Inc. Machine learning systems and methods for training with noisy labels
US10475165B2 (en) * 2017-04-06 2019-11-12 Disney Enterprises, Inc. Kernel-predicting convolutional neural networks for denoising
US10572979B2 (en) * 2017-04-06 2020-02-25 Pixar Denoising Monte Carlo renderings using machine learning with importance sampling
US10296578B1 (en) * 2018-02-20 2019-05-21 Paycor, Inc. Intelligent extraction and organization of data from unstructured documents
CN112785486A (zh) 2019-11-07 2021-05-11 英特尔公司 用于图像去噪声的自适应可变形核预测网络
CN113767417B (zh) * 2020-01-23 2025-01-03 百度时代网络技术（北京）有限公司 用于滤色器阵列图像去噪的深度残差网络
EP4016446B1 (en) * 2020-12-21 2025-06-25 Dassault Systèmes Intelligent denoising
CN114693850A (zh) * 2020-12-25 2022-07-01 英特尔公司 用于图像和视频处理的条件核预测网络和自适应深度预测

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11869171B2 (en) 2019-11-07 2024-01-09 Intel Corporation Adaptive deformable kernel prediction network for image de-noising
US12211172B2 (en) 2021-10-07 2025-01-28 Samsung Electronics Co., Ltd. Display device and operating method of the same

Also Published As

Publication number Publication date
JP2021077343A (ja) 2021-05-20
US20210142448A1 (en) 2021-05-13
DE102020129251A1 (de) 2021-05-12
US20240127408A1 (en) 2024-04-18
CN112785486A (zh) 2021-05-11
US11869171B2 (en) 2024-01-09

Similar Documents

Publication Publication Date Title
US20250103546A1 (en) Cache structure and utilization
KR102838677B1 (ko) 매트릭스 가속기 아키텍처를 위한 희소 최적화
US11869171B2 (en) Adaptive deformable kernel prediction network for image de-noising
US11709714B2 (en) Thread group scheduling for graphics processing
US20220207656A1 (en) Conditional kernel prediction network and adaptive depth prediction for image and video processing
KR20210059649A (ko) 그래픽 프로세싱 유닛을 위한 데이터 국부성 향상 기법
US20200294301A1 (en) Multi-tile graphics processor rendering
US11036545B2 (en) Graphics systems and methods for accelerating synchronization using fine grain dependency check and scheduling optimizations based on available shared memory space
US11409693B2 (en) Scalar core integration
JP7574523B2 (ja) メモリ効率を改善するための起動及びカーネルの動的な分割
US20200293456A1 (en) Preemptive page fault handling
US11227358B2 (en) Systems and methods for exploiting queues and transitional storage for improved low-latency high-bandwidth on-die data retrieval
CN113412475A (zh) 事务页错误处置
EP4020377A1 (en) Conditional kernel prediction network and adaptive depth prediction for image and video processing
JP2023046252A (ja) 浮動小数点計算のエミュレーション

Legal Events

Date Code Title Description
PA0109 Patent application

St.27 status event code: A-0-1-A10-A12-nap-PA0109

PG1501 Laying open of application

St.27 status event code: A-1-1-Q10-Q12-nap-PG1501

P22-X000 Classification modified

St.27 status event code: A-2-2-P10-P22-nap-X000

P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000

P13-X000 Application amended

St.27 status event code: A-2-2-P10-P13-nap-X000

PA0201 Request for examination

St.27 status event code: A-1-2-D10-D11-exm-PA0201

P22-X000 Classification modified

St.27 status event code: A-2-2-P10-P22-nap-X000

P22-X000 Classification modified

St.27 status event code: A-2-2-P10-P22-nap-X000

D21 Rejection of application intended

Free format text: ST27 STATUS EVENT CODE: A-1-2-D10-D21-EXM-PE0902 (AS PROVIDED BY THE NATIONAL OFFICE)

PE0902 Notice of grounds for rejection

St.27 status event code: A-1-2-D10-D21-exm-PE0902

E13 Pre-grant limitation requested

Free format text: ST27 STATUS EVENT CODE: A-2-3-E10-E13-LIM-X000 (AS PROVIDED BY THE NATIONAL OFFICE)

E13-X000 Pre-grant limitation requested

St.27 status event code: A-2-3-E10-E13-lim-X000

P11 Amendment of application requested

Free format text: ST27 STATUS EVENT CODE: A-2-2-P10-P11-NAP-X000 (AS PROVIDED BY THE NATIONAL OFFICE)

P11-X000 Amendment of application requested

St.27 status event code: A-2-2-P10-P11-nap-X000