Samsung electronics co., ltd. (20240185383). METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME simplified abstract
Contents
- 1 METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 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
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME
Organization Name
Inventor(s)
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 20240185383 titled 'METHOD OF PROCESSING IMAGE BASED ON SUPER-RESOLUTION WITH DEEP LEARNING AND METHOD OF PREDICTING CHARACTERISTIC OF SEMICONDUCTOR DEVICE USING THE SAME
Simplified Explanation
The abstract describes a method of processing an image based on super-resolution using a super-resolution convolutional neural network (SRCNN) to generate a high-resolution image from a low-resolution input image.
- The method involves performing a series of computing operations on the low-resolution input image using SRCNN to create a residual image.
- An interpolation operation is then performed on the low-resolution input image to generate an interpolation image.
- The residual image is added to the interpolation image to produce a high-resolution output image with a higher resolution than the input image.
- The SRCNN includes multiple computation layers, including convolutional layers and a deconvolutional layer for post-up-sampling.
Potential Applications
This technology can be applied in various fields such as medical imaging, satellite imaging, surveillance systems, and video enhancement.
Problems Solved
This technology addresses the issue of enhancing the resolution of low-quality images, improving image clarity and detail for better analysis and visualization.
Benefits
The method improves image quality, enhances details, and increases the resolution of images, leading to better image interpretation and analysis.
Potential Commercial Applications
Potential commercial applications include software development for image processing, video editing, medical imaging systems, and surveillance technology.
Possible Prior Art
One example of prior art in this field is the use of traditional interpolation methods to enhance image resolution, which may not produce as accurate or detailed results as the super-resolution method described in this patent application.
Unanswered Questions
How does this technology compare to other super-resolution methods on the market?
Answer: This article does not provide a direct comparison with other super-resolution methods, so it is unclear how this technology stacks up against existing solutions in terms of performance and efficiency.
What are the computational requirements for implementing this method in real-time applications?
Answer: The article does not delve into the computational resources needed to implement this method in real-time scenarios, leaving a gap in understanding the practical feasibility of the technology in various applications.
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.
