Unlock instant, AI-driven research and patent intelligence for your innovation.

Random asymptotic photon mapping image noise reduction method and system based on neural network

A neural network and photon mapping technology, used in image enhancement, image analysis, image data processing, etc., can solve the problem of increasing the difficulty of noise reduction, and achieve the effect of increasing stability, avoiding noise reduction constraints, and maintaining lighting details.

Active Publication Date: 2022-06-07
SHANDONG UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The special nature of random progressive photon mapping introduces multi-scale noise into the same rendering result, which increases the difficulty of noise reduction

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Random asymptotic photon mapping image noise reduction method and system based on neural network
  • Random asymptotic photon mapping image noise reduction method and system based on neural network
  • Random asymptotic photon mapping image noise reduction method and system based on neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] Embodiment 1, this embodiment provides a neural network-based random asymptotic photon mapping image noise reduction method;

[0040] A neural network-based stochastic asymptotic photon mapping image denoising method, including:

[0041] S100: Acquire a three-dimensional scene, and generate an image to be denoised based on random progressive photon mapping, where the image to be denoised includes a global photon-based rendering image and a caustics photon-based rendering image;

[0042] S200: Input the rendered image based on global photons into the pre-trained first multiple residual neural network, and output a global photon noise reduction image; input the rendered image based on caustics into the second multiple pre-trained multiple In the residual neural network, the caustics photon noise reduction image is output;

[0043] S300: Synthesize the global photon noise reduction image and the caustics photon noise reduction image to obtain a final rendered image.

[0...

Embodiment 2

[0116] Embodiment 2, this embodiment provides a neural network-based random asymptotic photon mapping image noise reduction system;

[0117] A stochastic asymptotic photon mapping image noise reduction system based on neural network, including:

[0118] a data generation module, which is configured to: acquire a three-dimensional scene, generate an image to be denoised based on random progressive photon mapping, and the image to be denoised includes a global photon-based rendering image and a caustics photon-based rendering image;

[0119] The noise reduction module is configured to: input the global photon-based rendering image into the pre-trained first multiple residual neural network, and output the global photon noise reduction image; input the caustics photon-based rendering image into In the pre-trained second multiple residual neural network, the caustics photon noise reduction image is output;

[0120] The synthesis module is configured to: synthesize the global phot...

Embodiment 3

[0122] Embodiment 3, this embodiment also provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor, and when the computer instructions are run by the processor, the first embodiment is completed. method described.

[0123] It should be understood that, in this embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors, DSPs, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices. , discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

[0124] The memory may include read-only memory and random access memory and provide instructions and data to the processor, and a portion of the memory ma...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a neural network-based random progressive photon mapping image noise reduction method and system. The rendered image of photons; the rendered image based on global photons is input into the pre-trained first multiple residual neural network, and the global photon noise reduction image is output; the rendered image based on caustic photons is input into the pre-trained first In the two-multiple residual neural network, the caustic photon noise reduction image is output; the global photon noise reduction image and the caustic photon noise reduction image are synthesized to obtain a final rendered image.

Description

technical field [0001] The present disclosure relates to the technical field of image photorealistic rendering, and in particular, to a neural network-based random asymptotic photon mapping image noise reduction method and system. Background technique [0002] The statements in this section merely mention background related to the present disclosure and do not necessarily constitute prior art. [0003] Stochastic Progressive Photon Mapping is a widely used physics-based photorealistic rendering method, which can effectively simulate complex optical effects such as caustics generated by the interaction of light and objects in the real world. It is a memory-friendly photon mapping. and an improved method for progressive photon mapping. [0004] However, as a biased rendering method, when inappropriate scene rendering parameters are selected or the number of rendering iterations is insufficient, stochastic progressive photon mapping often produces low-quality results due to la...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T15/00
CPCG06T15/00G06T2207/20081G06T2207/20084G06T5/70
Inventor 王璐曾峥徐延宁康春萌王贝贝孟祥旭
Owner SHANDONG UNIV
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com