Neural Network Image Enhancement with Re-noising
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Solution Overview
Problem
Legacy image enhancement technologies face challenges in preserving the original aesthetic and artistic intent of degraded images while improving image quality, particularly in remastering interlaced, noisy, and low-resolution content.
Innovation Solution
The implementation of a neural network-based image enhancement system that performs re-noising and image enhancement by adding noise to the color values of images, using a trained noise synthesizer and image restoration neural network to separate and process image and noise components independently, allowing for interpolation and enhancement of output image noise to produce high-quality enhanced images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image enhancement methods are used to improve image quality, then image resolution and clarity are enhanced, but the original aesthetic and artistic intent of the degraded image is lost
Solution Approach 1:
The patent segments the image enhancement process into distinct functional modules: a noise synthesizer that generates noise components, an interpolator that combines original and synthesized noise, and an enhancer that applies the combined noise to the degraded image. This segmentation allows independent optimization of each module to preserve both image quality and original aesthetic characteristics.
Solution Approach 2:
The patent applies preliminary action by pre-synthesizing noise components and interpolating them with the original noise before the main enhancement process. This preliminary preparation of noise characteristics ensures that the enhancement process can focus on improving image quality while the pre-prepared noise interpolation preserves the original aesthetic intent.
2Manufacturing precision
If noise is completely removed from degraded images, then image clarity is improved, but authenticity and artistic intent are compromised
Solution Approach 1:
The patent converts the harmful noise in degraded images into a beneficial element by synthesizing additional noise components and interpolating them with the original noise. This transformed noise is then applied to the enhanced image to preserve authenticity and artistic intent, turning the previously harmful noise into a feature that maintains reliability.
Solution Approach 2:
The patent changes the parameters of noise by synthesizing new noise components with different characteristics and interpolating them with the original noise at controlled ratios. This parameter transformation allows the noise to serve dual purposes: maintaining image clarity through removal of harmful noise while preserving authenticity through controlled reintroduction of characteristic noise.
3Manufacturing precision
If legacy video content is remastered to high resolution, then image quality is enhanced, but processing complexity and resource requirements increase
Solution Approach 1:
The patent creates a universal image enhancement system that can process various types of degraded images (interlaced, noisy, low-resolution) using the same multi-functional architecture. The noise synthesizer, interpolator, and enhancer work together in a unified framework that handles different image degradation types without requiring separate processing pipelines, thereby managing complexity efficiently.
Data Source
AI summary
According to one implementation, a system for performing re-noising and neural network (NN) based image enhancement includes a computing platform having a processing hardware and a system memory storing a software code, a noise synthesizer, and an image restoration NN. The processing hardware is configured to execute the software code to receive a denoised image component and a noise component extracted from a degraded image, to generate, using the noise synthesizer and the noise component, synthesized noise corresponding to the noise component, and to interpolate, using the noise component and the synthesized noise, an output image noise. The processing hardware is further configured to execute the software code to enhance, using the image restoration NN, the denoised image component to provide an output image component, and to re-noise the output image component, using the output image noise, to produce an enhanced output image corresponding to the degraded image.