Image Processing With De-Filter Networks for Personalized Effects
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Solution Overview
Problem
Existing image filter technologies lack the ability to provide personalized and effective filter effects, requiring users to have specific knowledge or relying on limited preset filters, which hampers user experience.
Innovation Solution
An image processing method utilizing an image de-filter network, such as a deep convolutional neural network, to remove existing filter effects and generate personalized filters based on feature extraction and mapping relationships, considering scene similarity and human face areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If preset filter method is used, then image processing can be performed with simple operation, but filter variety and personalization are limited
Solution Approach 1:
The system enables users to generate custom filters automatically by providing reference images and filter effect images, eliminating the need for manual parameter adjustment. The deep learning model automatically learns the mapping relationship between reference images and filter effects, creating personalized filters without requiring user expertise in image processing.
Solution Approach 2:
The patent transforms filters from fixed preset parameters to dynamic, learnable parameter sets. By using deep learning neural networks, the system automatically determines optimal filter parameters based on the input reference image and desired effect, allowing continuous adaptation to different imaging scenarios rather than relying on static preset values.
2Adaptability or versatility
If custom filter method with parameter adjustment is used, then filter personalization is achieved, but user knowledge requirement increases
Solution Approach 1:
The deep learning model performs automatic filter generation by processing reference images and filter effect images through neural network inference. The system self-determines the mapping relationship between input images and filter effects, completely eliminating the need for users to understand or adjust complex processing parameters.
Solution Approach 2:
The neural network serves as an intermediary between the user's simple image inputs and the complex filter generation process. Users only need to provide reference images and desired effects, while the neural network handles all the complex mathematical computations and parameter optimizations in the background.
3Manufacturing precision
If deep learning based filter generation is used, then filter personalization and rendering quality are improved, but processing time increases
Solution Approach 1:
The system performs preliminary filter extraction and feature learning during the training phase using large datasets of reference images and filter effects. Once trained, the neural network can quickly generate filters for new images by simply inferring from the pre-learned patterns, avoiding the need for time-consuming real-time analysis during actual filter application.
Solution Approach 2:
The neural network learns to copy and replicate successful filter mappings from training data. By storing learned relationships between reference images and filter effects in the network weights, the system can quickly generate new filters by copying relevant patterns rather than computing everything from scratch.
Data Source
AI summary
The present disclosure provides methods, apparatuses, and computer-readable mediums for image processing. In some embodiments, a method of image processing includes acquiring, from a user, a first image. The method further includes removing, using an image de-filter network, a filter effect applied to the first image to generate a second image. The method further includes obtaining, based on the first image and the second image, an image filter corresponding to the filter effect. The method further includes rendering a third image using the obtained image filter to output a fourth image.