Face Image Enhancement Using Feature-Guided Latent Denoising
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Solution Overview
Problem
Existing image enhancement methods for face images often lose a large number of original features due to difficulty in controlling denoising strength, resulting in a poor enhancement effect.
Innovation Solution
An image enhancement method that involves obtaining a latent variable of a face image, adding noise to it, extracting facial features, denoising the noised latent variable in conjunction with the facial features, and performing image reconstruction to obtain an enhanced face image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is gradually added to the face image until it becomes a random noise image, then the denoising process can be performed, but a large number of original features are lost due to difficulty in controlling denoising strength
Solution Approach 1:
The patent extracts facial features from the original image before the denoising process begins. These extracted features are then used as guidance during denoising to preserve important facial characteristics. This preliminary extraction and preservation of features solves the problem of feature loss by preparing the necessary information before the harmful denoising process occurs.
Solution Approach 2:
The patent implements a feedback mechanism where extracted facial features are continuously referenced during the denoising process. The denoising operation uses this feedback to adjust its strength and preserve features, rather than blindly removing all noise. This closed-loop control prevents excessive feature loss while maintaining enhancement effectiveness.
2Reliability
If denoising strength is increased to remove more noise, then the enhancement effect improves, but more original features are lost
Solution Approach 1:
The patent changes the parameter of denoising strength from a fixed high value to a dynamically adjusted value based on feature importance. By modifying this parameter during the process and using extracted features as a reference, the system achieves both strong denoising effect and feature preservation, resolving the contradiction between enhancement quality and feature accuracy.
Solution Approach 2:
The extracted facial features act as an intermediary between the noise removal process and the original image. This intermediary guides the denoising operation to remove noise while preserving important features, mediating between the conflicting goals of aggressive denoising and feature preservation.
Data Source
AI summary
This application provides an image enhancement method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product, which can be applied to various scenes, such as a cloud technology, artificial intelligence, intelligent transportation, and aided driving. The method includes the following operations: obtaining a latent variable of a to-be-enhanced face image, and adding noise to the latent variable, to obtain a noised latent variable of the face image, the face image being an image of a face of a target object; extracting a facial feature of the face in the face image; denoising the noised latent variable in conjunction with the facial feature, to obtain a denoised latent variable of the face image; and performing image reconstruction on the denoised latent variable to obtain an enhanced face image of the face image.