Wavelet Image Reconstruction for High-Frequency Detail Recovery
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Solution Overview
Problem
Existing image processing systems struggle to recover high-frequency details and remove artifacts effectively during resolution adjustments, as conventional methods fail to accurately predict sub-pixel information and address discontinuities and detail loss in upsampled images.
Innovation Solution
A wavelet frequency optimization model using a deep learning architecture with frequency and spatial domain paths predicts sub-pixel information and corrects artifacts by applying non-linear adjustments to frequency sub-bands, utilizing discrete wavelet transforms and residual blocks to reconstruct high-resolution images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional upsampling methods are used to restore image resolution, then image resolution is improved, but high-frequency details are lost and artifacts are introduced
Solution Approach 1:
The patent applies Discrete Wavelet Transform to segment the image into multiple frequency sub-bands (LL, LH, HL, HH). This segmentation allows selective processing of different frequency components, enabling recovery of high-frequency details that are lost during downsampling while maintaining the benefits of resolution restoration.
Solution Approach 2:
The patent transforms the image from the spatial domain to the frequency domain using wavelet transforms. This dimensional transformation enables the model to process and reconstruct high-frequency information that is not visible in the spatial domain, thereby recovering details lost during conventional upsampling operations.
2Reliability
If deep learning models with multiple paths are used to predict sub-pixel information, then artifact removal effectiveness is improved, but model complexity increases
Solution Approach 1:
The neural network is divided into distinct frequency domain and spatial domain paths, each processing specific sub-bands or image regions. This segmentation of processing tasks allows each path to specialize in predicting sub-pixel information for its specific domain, improving overall reliability while keeping individual path complexity manageable.
Solution Approach 2:
The wavelet-based reconstruction engine serves multiple functions: it performs upsampling, predicts sub-pixel information, removes artifacts, and reconstructs high-frequency details. By implementing a universal model that handles multiple tasks through frequency and spatial domain paths, the patent improves reliability without proportionally increasing complexity.
Data Source
AI summary
In various examples, wavelet prediction-based image reconstruction for image processing systems and applications is provided. A deep learning model may use derived frequency bands to predict sub-pixel-level information to perform predictive resampling as well as image/video artifact removal. The model may learn to predict missing frequency components while removing artifacts to generate resampled resolution image predictions based on the original input image. The model may comprise distinct frequency domain and spatial domain paths. The frequency domain path may process frequency domain sub-band images to introduce individualized non-linearity. Spatial domain prediction data may be generated based on the upsampled original input image. Substantive corrections may be applied by mapping the spatial domain prediction data into frequency sub-band images and the correcting sub-band images based on frequency domain prediction data. The resulting corrected sub-band images may be applied to an inverse DWT to reconstruct a resampled version.