Image Reconstruction via Dynamic Dictionary and Frequency Segmentation
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Solution Overview
Problem
Existing image reconstruction methods face challenges in adapting to different image scenes and require significant memory and computation resources, especially when using sparse representation techniques, which can lead to low-quality video streaming due to blocking effects and noise in limited bandwidth conditions.
Innovation Solution
An image processing method that dynamically constructs an image dictionary using a buffer of temporary images, decomposing them into high and low frequency components, and computes feature statistics to reconstruct images efficiently, reducing memory usage and improving quality by selecting representative images and discarding similar ones based on similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sparse representation technique is used to improve image reconstruction quality, then image quality is improved, but memory consumption and computation resources increase significantly
Solution Approach 1:
The patent segments the image processing task by separating low-frequency components (processed via traditional methods) from high-frequency components (processed via sparse representation). This segmentation allows sparse representation to be applied only where needed, reducing overall memory consumption while maintaining reconstruction quality.
Solution Approach 2:
The patent applies sparse representation partially - only to high-frequency image components rather than the entire image. This partial action reduces the computational burden and memory requirements compared to applying sparse representation to the complete image, while still achieving improved reconstruction quality.
2Manufacturing precision
If sparse representation technique is used to improve image reconstruction quality, then image quality is improved, but computation resources increase significantly
Solution Approach 1:
The patent segments the image processing task by separating low-frequency components (processed via traditional methods) from high-frequency components (processed via sparse representation). This segmentation allows computationally intensive sparse representation to be applied only where needed, reducing overall computation resources while maintaining reconstruction quality.
Solution Approach 2:
The patent applies sparse representation partially - only to high-frequency image components rather than the entire image. This partial action reduces the computational burden and memory requirements compared to applying sparse representation to the complete image, while still achieving improved reconstruction quality.
3Manufacturing precision
If large amount of reference images are collected to construct image feature dictionary, then image reconstruction quality is improved, but adaptability to different image scenes deteriorates
Solution Approach 1:
The patent implements a dynamic image dictionary construction mechanism that adapts to different image scenes. Instead of using a fixed large dictionary built from diverse reference images, the system dynamically constructs dictionaries based on the specific characteristics of the input image, improving adaptability while maintaining reconstruction quality.
Solution Approach 2:
The system performs self-service by automatically adapting its image dictionary to match the characteristics of the input image scene. The dictionary construction process is driven by the image itself rather than relying on pre-collected reference images, enabling the system to serve different scenes effectively without external reference data.
Data Source
AI summary
According to one exemplary embodiment, a method for image processing selects at least one image from a plurality of images, and stores the plurality of images into a buffer, until a storage space of the buffer reaches a criterion. When the storage space of the buffer reaches the criterion, a procedure of image processing is performed for a plurality of temporary images stored in the buffer, which includes constructing an image dictionary according to the at least an image selected, and performing an image recovery and reconstruction procedure for the plurality of temporary images in the buffer according to the image dictionary, thereby obtaining a plurality of reconstructed images.