Defocus Blur Estimation via Discrete Spatial Frequency Analysis

Solution Overview

Problem

Conventional techniques for estimating defocus blur in images consume significant computing resources, making them unsuitable for applications that require efficient processing.

Innovation Solution

The method involves selecting fixed spatial frequencies with discrete magnitudes on a polar grid to analyze images for blur, determining frequency responses, and computing probabilities for blur disc radii to estimate the amount of blur caused by lens defocus at each pixel, while reducing the number of channels in the frequency response image.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional techniques are used for defocus blur estimation, then measurement precision is improved, but computing resource consumption increases

Engineering Contradiction:

Improvedefocus blur estimation accuracyVSAvoidcomputing resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The frequency spectrum is segmented into specific spatial frequency bands (low, medium, high frequencies) that are most relevant for defocus blur detection. By focusing computational effort on these specific frequency segments rather than analyzing the entire spectrum, the method achieves accurate blur estimation while reducing overall computing resource consumption.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the image from spatial domain to frequency domain using Fourier transform, changing the parameter representation. This allows defocus blur to be detected through frequency response analysis at specific spatial frequencies, which is computationally more efficient than conventional spatial domain methods while maintaining measurement precision.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If conventional techniques are used for defocus blur estimation, then measurement precision is improved, but processing time increases

Engineering Contradiction:

Improvedefocus blur estimation accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The frequency spectrum is segmented into specific spatial frequency bands (low, medium, high frequencies) that are most relevant for defocus blur detection. By focusing computational effort on these specific frequency segments rather than analyzing the entire spectrum, the method achieves accurate blur estimation while reducing overall computing resource consumption.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms the image from spatial domain to frequency domain using Fourier transform, changing the parameter representation. This allows defocus blur to be detected through frequency response analysis at specific spatial frequencies, which is computationally more efficient than conventional spatial domain methods while maintaining measurement precision.

Inventive Principle:

Principle #35Parameter changes

3Use of energy by moving object

If fixed spatial frequencies with discrete magnitudes are selected, then computing resources are reduced, but frequency response analysis becomes more constrained

Engineering Contradiction:

Improvecomputing resource consumptionVSAvoidfrequency response analysis flexibility

Core Design Contradiction:

Use of energy by moving objectVSAdaptability or versatility

Solution Approach 1:

The patent transforms the image from spatial domain to frequency domain using Fourier transform, changing the parameter representation. This allows defocus blur to be detected through frequency response analysis at specific spatial frequencies, which is computationally more efficient than conventional spatial domain methods while maintaining measurement precision.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces frequency response as an intermediary parameter that connects the selected spatial frequencies to the defocus blur estimation. By analyzing how the image responds at these specific frequencies, the method achieves efficient computation without sacrificing the ability to accurately characterize blur, as the frequency response serves as a mediator that captures essential blur information.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20160182807A1Image Defocus Blur Estimation

Publication Date: 2016.06.23 ADOBE INC

AI summary

Image defocus blur estimation techniques are described. In one or more implementations, fixed spatial frequencies that are usable to analyze an image for blur are selected. The selected spatial frequencies are input to a function used to determine frequency responses for pixels of the image. The frequency responses indicate a response of the pixels around a given pixel to the selected spatial frequencies. The spatial frequencies that are selected may be limited to spatial frequencies having a frequency magnitude from a set of discrete values. The discrete values may, for instance, range from a minimum frequency magnitude to a maximum frequency magnitude, and be spaced apart by a frequency magnitude increment. A number of frequencies that are selected at each magnitude may also be based on the frequency magnitude increment.