Radiographic Image Enhancement via Adaptive Contrast Coefficients
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Solution Overview
Problem
Existing image enhancement methods in radiography fail to adequately enhance contrast in dark regions and often introduce noise, leading to unclear images due to insufficient edge enhancement in thick objects.
Innovation Solution
A method involving normalization, adaptive low-pass filtering, relative standard deviation thresholding, non-linear contrast enhancement, and adaptive enhancement coefficients to specifically enhance edge details in all gradation regions while reducing noise, particularly in dark areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional dot operation methods (gradation transformation, histogram equalization) are used to enhance contrast, then contrast in certain gradation ranges is improved, but noise is introduced and enhancement is uneven across different regions
Solution Approach 1:
The patent applies local quality by calculating adaptive enhancement coefficients for different pixel regions based on their gradation values. Pixels in dark regions (low gradation values) receive higher enhancement coefficients, while pixels in bright regions receive lower coefficients. This regional differentiation allows targeted contrast enhancement in dark areas without uniformly amplifying noise across the entire image.
Solution Approach 2:
The patent implements dynamics by using adaptive enhancement coefficients that vary dynamically based on the local gradation value of each pixel. The enhancement coefficient is calculated as a function of the pixel's gradation value, allowing the contrast enhancement process to adapt to the specific characteristics of each region rather than applying a static global transformation.
2Manufacturing precision
If edge enhancement methods (gradient method, Laplacian operator, high-pass filtering) are used to clarify edges, then edge sharpness is improved, but noise is enhanced and weak edges in dark regions are not sufficiently enhanced
Solution Approach 1:
The patent applies local quality by calculating adaptive enhancement coefficients for different pixel regions based on their gradation values. Pixels in dark regions (low gradation values) receive higher enhancement coefficients, while pixels in bright regions receive lower coefficients. This regional differentiation allows targeted contrast enhancement in dark areas without uniformly amplifying noise across the entire image.
Solution Approach 2:
The patent implements parameter changes by modifying the enhancement coefficient as a function of the pixel's gradation value. The enhancement coefficient varies dynamically based on the local characteristics of each pixel, allowing the contrast enhancement process to adapt to the specific characteristics of each region rather than applying a static global transformation.
3Measurement precision
If conventional edge enhancement methods are applied to thick objects, then edge detection is improved, but contrast enhancement in dark regions is insufficient due to constant enhancement ratios
Solution Approach 1:
The patent applies local quality by calculating adaptive enhancement coefficients for different pixel regions based on their gradation values. Pixels in dark regions (low gradation values) receive higher enhancement coefficients, while pixels in bright regions receive lower coefficients. This regional differentiation allows targeted contrast enhancement in dark areas without uniformly amplifying noise across the entire image.
Solution Approach 2:
The patent implements parameter changes by modifying the enhancement coefficient as a function of the pixel's gradation value. The enhancement coefficient varies dynamically based on the local characteristics of each pixel, allowing the contrast enhancement process to adapt to the specific characteristics of each region rather than applying a static global transformation.
Data Source
AI summary
A method of image information enhancement in radiography relates to image information processing techniques in radiography. The method comprising steps of: normalizing an acquired image A(x,y) to form a normalized image B(x,y); filtering the normalized image B(x,y) by a low-pass filter to obtain an filtered image C(x,y); calculating a relative standard deviation for each pixel in the image A(x,y), three times the relative standard deviation being an edge threshold for each pixel; thresholding a difference image obtained by subtracting the filtered image C(x,y) from the normalized image B(x,y) by using the edge threshold for each pixel to form a threshold-processed image D(x,y); enhancing a contrast of the threshold-processed image D(x,y) by using a non-linear function to form a contrast-enhanced image E(x,y); determining a enhancement coefficient a(x,y); obtaining a edge-enhanced image F(x,y) by multiplying the enhancement coefficient a(x,y), the contrast-enhanced image E(x,y) and the filtered image C(x,y); and generating a resulting image by multiplying a sum of the edge-enhanced image F(x,y) and the filtered image C(x,y) with the maximum value Amax As compared with the prior arts, the inventive method has a fast processing speed for image information enhancement and a simple algorithm, images clearly, eliminates noises in the images, and satisfies the requirements of relatively more enhancement to the contrast of the dark regions in the scanned images.