Cross-Attention Image Restoration for Low-Resolution Character Recovery
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Solution Overview
Problem
Existing image processing technologies struggle to effectively restore low-resolution images, particularly those with distorted or blurred subjects, such as license plates, due to challenges in accurately enhancing resolution and recognizing characters.
Innovation Solution
An electronic device employs an image restoration model that includes a first encoder for extracting global context information from a high-resolution image, a second encoder for extracting local feature information from a low-resolution image, and a decoder that utilizes multi-head cross attention to generate a high-resolution output image based on this information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing technologies are used to restore low-resolution images, then the processing is simple and fast, but the resolution enhancement is insufficient and character recognition accuracy deteriorates
Solution Approach 1:
The model is segmented into three distinct functional modules: a first encoder for extracting global context features from high-resolution images, a second encoder for extracting local features from low-resolution images, and a decoder for reconstructing the restored image. This segmentation allows each module to specialize in specific tasks, improving overall restoration quality while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces feature extraction encoders and a cross-attention mechanism as intermediary components between the input images and the final restoration output. These intermediaries transform the input images into meaningful feature representations that capture both global context and local details, enabling accurate resolution enhancement without directly manipulating pixel values.
2Manufacturing precision
If simple encoding is used, then the model is computationally efficient, but global context information is lost leading to poor restoration quality
Solution Approach 1:
The patent extracts global context information separately using a dedicated first encoder that processes high-resolution images. This extracted global context is then integrated with local features through a cross-attention mechanism, ensuring that comprehensive contextual information is captured without requiring excessively complex processing of the entire image at once.
Solution Approach 2:
The model performs preliminary feature extraction in the encoding stage, where both global context and local features are extracted and prepared before the restoration process. This preliminary action organizes the information in advance, reducing the computational burden during the actual image restoration and enabling more efficient processing.
3Manufacturing precision
If high computational power is used for detailed feature extraction, then restoration quality improves, but processing speed decreases
Solution Approach 1:
The patent applies local quality by using a second encoder to extract local features specifically from the low-resolution input image, while the first encoder handles global context from high-resolution reference images. This division allows computationally intensive global context extraction to be performed on reference data, while local feature extraction focuses only on the specific region needing restoration, improving processing efficiency.
Data Source
AI summary
According to an embodiment, an electronic device receives a request to restore a second input image with a first resolution representing a specified portion of a first input image to an output image with a second resolution exceeding the first resolution. The electronic device, based on the received request, executes an image restoration model including a first encoder for extracting first feature information from the first input image, a second encoder for extracting second feature information from the second input image, and a decoder for generating the output image with the second resolution based on multi head cross attention between the first feature information and the second feature information. The electronic device provides the output image with the second resolution obtained based on the execution of the image restoration model, as a response to the request.