Multi-scale Image Contrast Enhancement via Local Statistical Measures
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Solution Overview
Problem
Existing multi-scale image enhancement methods, particularly in medical radiographic imaging, often result in distortions and unnatural appearances due to excessive non-linearity at grey value transitions, especially near sharp edges or lines.
Innovation Solution
The method calculates a statistical measure for translation difference image pixel values, which reflects elementary contrast, and uses this measure to apply a non-linear enhancement function, reducing distortions by only modifying one value instead of all translation difference values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If non-linear conversion functions are applied to enhance image contrast, then image quality is improved, but distortions occur at grey level transitions
Solution Approach 1:
The patent applies different processing treatments to different regions of the image based on local characteristics. Translation difference images are created to identify regions with significant grey level transitions, and non-linear modification is selectively applied only to regions that do not exhibit such transitions, thereby preserving local quality and avoiding distortions at critical boundaries.
Solution Approach 2:
Instead of applying non-linear conversion functions to the entire image, the patent applies the modification only to specific regions identified through translation difference analysis. This partial action approach enhances contrast in appropriate areas while leaving sensitive transition regions unchanged, thus improving overall image quality without introducing harmful distortions.
2Manufacturing precision
If non-linear modification is applied to all translation difference images, then contrast enhancement is achieved, but calculation time increases
Solution Approach 1:
The patent identifies specific regions within translation difference images that require contrast enhancement and applies non-linear modification only to those regions. By using translation difference analysis to locate areas without significant grey level transitions, the method selectively processes only the necessary portions of the image, thereby achieving contrast enhancement while minimizing calculation time.
Solution Approach 2:
The patent applies non-linear modification to only a subset of translation difference images rather than processing all of them. This partial action is determined by analyzing translation differences to identify regions where contrast enhancement is beneficial and safe, thus achieving the desired contrast improvement without the computational burden of processing the entire image set.
Data Source
Figure 1
AI summary
A multi-scale image processing algorithm for enhancing contrast in an electronic representation of an image represented by a) decomposing said digital image into a set of detail images at multiple resolution levels and a residual image at a resolution level lower than said multiple resolution levels, b) processing at least one pixel of said detail images, c) computing a processed image by applying a reconstruction algorithm to the residual image and the processed detail images, said reconstruction algorithm being such that if it were applied to the residual image and the detail images without processing, then said digital image or a close approximation thereof would be obtained. The processing comprises the steps of: d) calculating for said pixel, at least one statistical measure for two or more translation difference image pixel values within a neighbourhood of said pixel; and e) modifying the value of said pixel of said detail images as a function of said statistical measure and said value of said pixel of said detail images.