Image Restoration via Saturation-Aware Segmented Filtering
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Solution Overview
Problem
Digital cameras face challenges in enhancing image quality due to optical point spread function (PSF) variations and blur, especially in low-cost color video cameras with single solid-state image sensors and mosaic filters, which affect the reconstruction of full-color images.
Innovation Solution
A method involving image filtering operations, including deconvolution filters and high-pass filters, is applied to pixel values from mosaic image sensors to generate enhanced output images by detecting regions of saturation and assigning weights, while also compressing and decompressing pixel values to reduce memory requirements and improve image restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deconvolution filtering is applied to restore image sharpness, then image blur is reduced, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the image into multiple regions based on saturation detection, applying different filtering operations to different regions. Saturated regions use one filtering approach while non-saturated regions use another, reducing overall computational complexity by avoiding unnecessary processing in all regions while maintaining quality where needed.
Solution Approach 2:
The patent applies local quality by detecting saturation regions and applying tailored filtering operations specifically to non-saturated regions while using different processing for saturated regions. This localized approach optimizes image quality in critical areas without unnecessarily processing the entire image, reducing computational burden.
2Manufacturing precision
If multiple filtering operations are applied to enhance image quality, then image restoration improves, but processing time increases
Solution Approach 1:
The patent segments the image processing into two distinct paths based on saturation detection: one for saturated regions and one for non-saturated regions. This segmentation allows parallel processing and avoids redundant filtering operations, reducing total processing time while maintaining image quality enhancement.
Solution Approach 2:
The patent applies partial action by selectively applying filtering operations only to non-saturated regions where image quality enhancement is most beneficial, while using simplified processing for saturated regions. This partial approach avoids excessive processing in regions where it would not improve quality, reducing overall processing time.
3Quantity of substance
If image data is compressed to reduce memory usage, then memory requirements decrease, but data loss may occur
Solution Approach 1:
The patent segments the image data processing by detecting saturation regions and applying different compression or processing strategies to different regions. This allows selective compression that preserves critical information in non-saturated regions while reducing memory usage in saturated regions, balancing data loss and memory requirements.
Data Source
AI summary
A method for imaging includes receiving an input image that includes a matrix of pixels (32) having input pixel values. The input pixel values are processed so as to detect a region of saturation in the input image. A first image filtering operation is applied to the input pixel values of at least the pixels that are outside the region of saturation, so as to generate first filtered pixel values. A second image filtering operation is applied to the input pixel values of at least the pixels that are within the region of saturation, so as to generate second filtered pixel value. An enhanced output image is generated by stitching together the first and second filtered pixel values.