Image Deconvolution Using Two-Color Priors
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Solution Overview
Problem
Existing image deblurring techniques often fail to effectively address image blur caused by camera shake, subject movement, and limited camera resolution, leading to unwanted artifacts and over-smoothing, especially when using non-blind deconvolution methods.
Innovation Solution
The use of a two-color prior model, where each pixel's color is formulated as a linear combination of the two most prevalent colors in its neighborhood, decouples edge sharpness from edge strength, reducing over-smoothing and noise, and is applied during deconvolution to produce a deblurred image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-blind deconvolution is used to deblur images, then deblurring can be achieved, but unwanted artifacts such as ringing are generated
Solution Approach 1:
The patent applies local color statistics derived from natural image statistics as priors during deconvolution. Instead of using a single global prior, the method computes color priors locally for different regions of the image, allowing each region to be deblurred according to its specific color distribution characteristics. This local adaptation reduces ringing artifacts while maintaining deblurring effectiveness.
Solution Approach 2:
The patent changes the parameter space by formulating pixel color as a linear combination of the two most prevalent colors in the neighborhood. This transforms the color representation from a simple RGB value to a mixture model with two dominant colors, which provides better constraints during deconvolution and reduces artifacts like ringing while maintaining sharp edges.
2Manufacturing precision
If traditional deconvolution methods are applied to reduce blur, then image sharpness can be improved, but over-smoothing occurs
Solution Approach 1:
The method computes local color statistics and forms color priors specific to each pixel's neighborhood. This local prior approach allows the deconvolution to adapt to the specific color distribution in each region, preserving edges and textures while reducing over-smoothing that occurs with global smoothing methods.
Solution Approach 2:
The patent introduces color priors as an intermediary constraint between the blurred input image and the sharp output image. These color priors, derived from natural image statistics, act as a mediator that guides the deconvolution process to produce sharp images while preventing over-smoothing by constraining the solution space to physically plausible color distributions.
3Manufacturing precision
If color priors are used to constrain deconvolution, then edge sharpness can be maintained, but computational complexity increases
Solution Approach 1:
The patent simplifies the color representation by assuming each pixel is a linear combination of only two prevalent colors in its neighborhood. This reduces the color space from three dimensions (RGB) to essentially one dimension (mixing ratio), which significantly reduces the computational complexity of calculating and applying color priors while still maintaining edge sharpness.
Solution Approach 2:
By computing color priors locally for each pixel based on its neighborhood's two prevalent colors, the method avoids the need for complex global color models. This local approximation reduces computational burden while maintaining the ability to preserve sharp edges through region-specific color constraints.
Data Source
AI summary
Described are techniques for image deconvolution to deblur an image given a blur kernel. Localized color statistics derived from the image to be deblurred serve as a prior constraint during deconvolution. A pixel's color is formulated as a linear combination of the two most prevalent colors within a neighborhood of the pixel. This may be repeated for many or all pixels in an image. The linear combinations of the pixels serve as a two-color prior for deconvolving the blurred image. The two-color prior is responsive to the content of the image and it may decouple edge sharpness from edge strength.