18366286. PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)
Contents
- 1 PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS - 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
PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18366286 titled 'PRESERVING DETAIL IN DENOISED IMAGES FOR CONTENT GENERATION SYSTEMS AND APPLICATIONS
Simplified Explanation
Approaches presented herein provide for the maintaining of fine details that might be removed by a denoiser used to reduce an amount of noise in an image. An input image can be provided to a denoiser, and can also can be simultaneously processed to extract pixel data that may correspond to fine detail or high frequency features. Individual pixels of an image can have a value determined for a material property sampled for that pixel location, and that value can be compared against an average material property value determined for neighboring pixels. The ratio of material values can be multiplied by the value of a corresponding pixel of the denoised image, for any or all pixel locations, to obtain final pixel values for an output image that include less noise than the original image but represent fine detail that may otherwise have been lost during the denoising process.
- Fine details in images are preserved by a novel approach that combines denoising with material property analysis.
- Pixel values are adjusted based on material properties of neighboring pixels to enhance fine detail in the final output image.
Potential Applications
This technology can be applied in:
- Medical imaging for preserving fine details in diagnostic images.
- Forensic analysis to enhance details in surveillance footage.
Problems Solved
- Prevents loss of fine details during image denoising process.
- Improves image quality without sacrificing noise reduction.
Benefits
- Enhanced image quality with preserved fine details.
- Improved accuracy in image analysis and interpretation.
Potential Commercial Applications
- Photography and image editing software.
- Security and surveillance systems for clearer image capture.
Possible Prior Art
There may be prior art related to image denoising techniques and material property analysis in image processing, but specific examples are not provided in this context.
Unanswered Questions
How does this technology compare to existing denoising methods in terms of computational efficiency?
The article does not address the computational efficiency of this approach compared to traditional denoising methods.
What impact does the incorporation of material property analysis have on the overall image processing time?
The article does not mention the potential impact on image processing time when material property analysis is included in the denoising process.
Original Abstract Submitted
Approaches presented herein provide for the maintaining of fine details that might be removed by a denoiser used to reduce an amount of noise in an image. An input image can be provided to a denoiser, and can also can be simultaneously processed to extract pixel data that may correspond to fine detail or high frequency features. Individual pixels of an image can have a value determined for a material property sampled for that pixel location, and that value can be compared against an average material property value determined for neighboring pixels. The ratio of material values can be multiplied by the value of a corresponding pixel of the denoised image, for any or all pixel locations, to obtain final pixel values for an output image that include less noise than the original image but represent fine detail that may otherwise have been lost during the denoising process.
