Image Processing Apparatus Frequency-Domain Region Optimization
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Solution Overview
Problem
Portable camera users experience image blurring due to camera shake and unstable support, which existing methods struggle to effectively address through image deconvolution and trajectory estimation.
Innovation Solution
An image processing method and apparatus that acquires regions with high textural similarity and different depths, performs frequency-domain conversion, and optimizes the image based on the frequency-domain signals, depth, and focusing distance to reduce blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequency-domain conversion and optimization are performed on multiple regions with different depths, then image definition is improved, but computational complexity increases
Solution Approach 1:
The image is divided into multiple regions based on depth information, with each region processed independently through frequency-domain conversion. This segmentation allows targeted optimization of different depth zones while managing computational load by processing regions separately rather than the entire image at once.
Solution Approach 2:
Different processing strategies are applied to different regions based on their depth characteristics. Regions with similar depth values are grouped together and processed with appropriate frequency-domain operations, optimizing local image quality while considering the specific depth-related blur characteristics of each region.
2Manufacturing precision
If regions with different depths are processed separately, then depth-specific blur is reduced, but processing time increases
Solution Approach 1:
The processing follows a systematic sequence: first identifying regions by depth, then performing frequency-domain conversion on each region in turn, and finally combining results. This periodic structured approach ensures depth-specific blur is addressed systematically while maintaining efficient processing through clear stage separation.
Solution Approach 2:
Depth information is extracted and regions are identified before the actual frequency-domain processing begins. This preliminary organization of regions by depth characteristics allows subsequent processing to be more efficient, as the grouping is already established and ready for batch processing.
Data Source
AI summary
Embodiments of the present application provide image processing methods and apparatus. A image processing method disclosed herein comprises: acquiring, from an image, two regions which have a textural similarity higher than a first value and have different depths; performing frequency-domain conversion on each of the regions, to obtain a frequency-domain signal of each region; and optimizing the image at least according to the frequency-domain signal of each region, the depth of each region and a focusing distance of the image.