View source for 18448590. METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Jinseok Hong of SUWON-SI (KR)|Jinseok Hong of SUWON-SI (KR)]][[Category:Jinseok Hong of SUWON-SI (KR)]]

[[:Category:Junhee Seok of SEOUL (KR)|Junhee Seok of SEOUL (KR)]][[Category:Junhee Seok of SEOUL (KR)]]

[[:Category:Jangwon Seo of SEOUL (KR)|Jangwon Seo of SEOUL (KR)]][[Category:Jangwon Seo of SEOUL (KR)]]

[[:Category:Kyuil Lee of SUWON-SI (KR)|Kyuil Lee of SUWON-SI (KR)]][[Category:Kyuil Lee of SUWON-SI (KR)]]

==METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18448590 titled 'METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME

The abstract describes a method of processing an image using super-resolution techniques, specifically utilizing a Super-Resolution Convolutional Neural Network (SRCNN) to enhance image quality.

* The method involves performing multiple computing operations on a low-resolution input image to generate a residual image.
* An interpolation operation is then applied to the low-resolution input image to create an interpolation image.
* The residual image is added to the interpolation image to produce a high-resolution output image.
* The SRCNN consists of computation layers, including convolutional layers and a deconvolutional layer for post-up-sampling.
* The deconvolutional layer is the final computation layer in the network.

Potential Applications:
- Enhancing image quality in photography and video production.
- Improving the clarity of medical imaging for diagnostic purposes.
- Enhancing satellite imagery for better analysis and monitoring.

Problems Solved:
- Addressing the issue of low-resolution images lacking detail and clarity.
- Providing a solution for upscaling images without losing quality.

Benefits:
- Improved image quality for various applications.
- Enhanced visual experience for users viewing the images.
- Increased accuracy in image analysis and interpretation.

Commercial Applications:
"Super-Resolution Image Processing Technology for Enhanced Visual Quality in Various Industries"

Prior Art:
Research on super-resolution techniques in image processing and computer vision.

Frequently Updated Research:
Ongoing advancements in deep learning and neural network technologies for image enhancement and super-resolution techniques.

Questions about Super-Resolution Image Processing Technology:
1. How does the SRCNN differ from traditional image upscaling methods?
2. What are the potential limitations of using super-resolution techniques in image processing?


==Original Abstract Submitted==

A method of processing an image based on super-resolution includes: sequentially performing a plurality of computing operations on a low-resolution input image using a super-resolution convolutional neural network (SRCNN) to generate a residual image, performing an interpolation operation on the low-resolution input image to generate an interpolation image and adding the residual image to the interpolation image to generate a high-resolution image. The high-resolution output image has a resolution higher than that of the low-resolution input image. The SRCNN includes a plurality of computation layers for performing the plurality of computing operations. The plurality of computation layers include a plurality of convolutional layers, and a deconvolutional layer for post-up-sampling. The deconvolutional layer is a last computation layer among the plurality of computation layers.



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