Adaptive Image Filter Resolving Noise-Edge Tradeoff
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Image capture devices face challenges in filtering noise from image information due to physical limitations and increased noise in compact image sensor modules, with existing low pass and high pass filtering methods either blurring images or enhancing noise.
Innovation Solution
Adaptive filtering techniques that compare each pixel's information to surrounding pixels, computing differences and applying a filter with adjustable low pass and high pass components based on threshold values to improve image quality on a pixel-by-pixel basis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If low pass filtering is applied to captured image information, then the amount of noise in the image is reduced, but the image becomes blurred by destroying sharp edges containing high frequency signals
Solution Approach 1:
The patent applies different filtering strengths to different regions of the image based on local characteristics. By analyzing the variance of pixel values in surrounding regions, the filter adapts its behavior: applying stronger low-pass filtering in homogeneous regions to reduce noise, while applying weaker filtering or preserving high-frequency components in regions with edges or high variance to maintain sharpness. This local adaptation resolves the contradiction by making the filtering quality spatially variable rather than uniform.
Solution Approach 2:
The patent employs dynamic filtering where the filtering parameters are not fixed but adapt based on local image characteristics. The filter dynamically adjusts its low-pass and high-pass components by comparing computed differences to threshold values, allowing the filtering behavior to change from pixel to pixel. This dynamic approach enables the system to reduce noise in smooth areas while preserving edges in high-contrast areas, resolving the static contradiction between noise reduction and edge preservation.
2Manufacturing precision
If high pass filtering is applied to captured image data, then sharp edges and contrast are enhanced, but the noise is inevitably enhanced as well
Solution Approach 1:
The patent applies high-pass filtering selectively only in regions where it is beneficial. By computing the variance or gradient magnitude in local neighborhoods, the filter identifies edge regions and applies high-pass enhancement there to sharpen edges and improve contrast. In homogeneous regions with low variance, the high-pass component is suppressed or set to zero, preventing noise enhancement. This spatially selective application of high-pass filtering resolves the contradiction by making edge enhancement location-dependent.
Solution Approach 2:
The patent uses dynamic control of the high-pass filter component based on local image statistics. The filtering parameters are adjusted pixel-by-pixel or region-by-region based on computed differences and threshold comparisons. This allows the system to enhance edges dynamically where needed while suppressing noise enhancement in regions where edges are absent, resolving the contradiction between edge enhancement and noise control through adaptive parameter adjustment.
3Volume of moving object
If compact image sensor modules are used to meet the demand for smaller image capture devices, then the device size is reduced, but the amount of noise captured within the image information increases significantly
Solution Approach 1:
The patent introduces an adaptive filtering process as an intermediary between image capture and final image output. This intermediary processing stage analyzes the captured image data and applies context-dependent filtering to reduce noise while preserving important features. The filter acts as a mediator that cleans up the noisy signal from compact sensors without simply blurring the entire image, by using local variance analysis to distinguish noise from meaningful image content and applying appropriate filtering only where needed.
Data Source
AI summary
This disclosure describes adaptive filtering techniques to improve the quality of captured imagery, such as video or still images. In particular, this disclosure describes adaptive filtering techniques that filter each pixel as a function of a set of surrounding pixels. An adaptive image filter may compare image information associated with a pixel of interest to image information associated with a set of surrounding pixels by, for example, computing differences between the image information associated with the pixel of interest and each of the surrounding pixels of the set. The computed differences can be used in a variety of ways to filter image information of the pixel of interest. In some embodiments, for example, the adaptive image filter may include both a low pass component and high pass component that adjust as a function of the computed differences.