Deep Learning Framework for Digital Image Restoration
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Solution Overview
Problem
Conventional digital image editing systems face challenges in accuracy, efficiency, and flexibility when restoring degraded images, particularly in addressing local defects and global imperfections.
Innovation Solution
The system employs a deep learning framework that utilizes a multistep process involving a defect detection neural network and a global correction neural network to restore degraded digital images. This includes generating a segmentation map for local defects, using an inpainting model to fill them, and correcting global imperfections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital image editing systems utilize retouching techniques to fill missing or damaged pixels, then particular scratches or blemishes can be removed one at a time, but the systems experience impediments in accuracy, efficiency, and flexibility
Solution Approach 1:
The system segments the image restoration process into distinct functional modules: a defect detection neural network that identifies local defects, an inpainting model that repairs them, and a global correction neural network that addresses overall image quality. This segmentation allows each component to specialize in specific tasks, improving both accuracy and efficiency simultaneously
Solution Approach 2:
The patent replaces manual retouching operations with automated deep learning models. The defect detection neural network automatically identifies scratches and blemishes without human intervention, and the inpainting model automatically generates repair pixels, eliminating the need for manual pixel-by-pixel editing while maintaining high accuracy
2Adaptability or versatility
If conventional digital image editing systems manually edit each defect, then specific local defects can be addressed, but the process becomes time-consuming and less flexible for handling multiple defect types
Solution Approach 1:
The defect detection neural network is designed with multi-functionality to detect various types of local defects including scratches, blemishes, dust, and tears simultaneously. The global correction neural network handles multiple types of global imperfections such as blur, noise, and color fading in a single pass, providing universal restoration capability that is both flexible and time-efficient
Solution Approach 2:
The system performs preliminary defect detection and classification before restoration. The defect detection neural network预先 identifies and locates all defects in the image, creating a segmentation map that guides subsequent inpainting operations. This preliminary action enables the system to plan and execute restorations efficiently without time-consuming trial-and-error editing
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.