Image magnification device and method with super-resolution magnification mechanism
By performing deep learning on images through a neural network system and combining the operations of magnification, neural network and enhancement modules, the problem of insufficient image magnification resolution in existing technologies is solved, and the clarity and details of high-resolution images are improved.
CN115115505BActive Publication Date: 2025-10-17REALTEK SEMICON CORP
2 Cites 0 Cited by
Patent Information
- Application Number
- CN202110287460.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-03-17
- Publication Date
- 2025-10-17
- Estimated Expiration
- 2041-03-17
AI Technical Summary
Technical Problem
Existing image magnification technology cannot effectively improve the resolution, resulting in blurry, unclear edges and noise in the magnified image.
Method used
An image magnification device and method with a super-resolution magnification mechanism is adopted, deep learning is performed through a neural network system, and image magnification is performed according to image characteristics, including the combined operation of an amplification module, a neural network module and an enhancement module to generate the final output image residual and enhance the magnified image.
Benefits of technology
It realizes enhanced processing of different image characteristics, improves image resolution, reduces noise and blur, and improves image clarity and detail.
✦ Generated by Eureka AI based on patent content.
Abstract
The present application relates to an image magnification device and method with super-resolution magnification mechanism. The method comprises: receiving an input image by a magnification module of a neural network system, performing image magnification, and generating a magnified image; receiving the input image by a front-end convolution path included in a neural network module of the neural network system to perform convolution operation and generate a front-end operation output result; receiving the front-end operation output result by a plurality of branch convolution paths included in the neural network module respectively to perform convolution operation and generate a plurality of sets of output image residuals; weighting and mixing the output image residuals by a mixing module included in the neural network module according to weight settings related to a plurality of image regions of the input image to generate a set of final output image residuals; and enhancing the magnified image according to the set of final output image residuals by an enhancement module to generate an output magnified image.
Need to check novelty before this filing date? Find Prior Art