Low-illumination color image enhancement method based on local extreme value
A local extremum and color image technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of inaccurate illuminance image estimation, loss of details in high-brightness areas of enhanced images, and insufficient contrast enhancement in low-brightness areas. Achieve the effect of improving the enhancement effect, changing the histogram distribution, brightness and contrast enhancement
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Embodiment 1
[0054] The existing method has the problem of inaccurate estimation of the contrast illuminance image, resulting in the loss of details in the high-brightness area of the enhanced image, and insufficient contrast enhancement in the low-brightness area; therefore, this application proposes a low-illuminance color image enhancement method to overcome the above problems, the details are as follows:
[0055] A low-illuminance color image enhancement method based on local extremum, comprising the following steps:
[0056] Step 1: convert the original color image from RGB space to YUV space, and extract the intensity channel Y of YUV space as grayscale image I;
[0057] Step 2: Use the local extremum filter whose kernel gradually increases to iteratively filter the grayscale image I, and use the filtering result as the illuminance component L of the image;
[0058] Step 3: Separate the reflection component R from the grayscale image I according to the illuminance component L;
[...
Embodiment 2
[0062] Based on Embodiment 1, consider how to perform illuminance processing, image conversion, and how to optimize image details in this method. The details are as follows:
[0063] Such as Figure 2-6 As shown, step 1 includes the following steps:
[0064] Step 1.1: Get the original color image S ∈ R m×n , m×n represents the length and width of the original color image S;
[0065] Step 1.2: Convert the original color image from RGB space to YUV space, and extract the intensity channel Y as grayscale image I.
[0066] Step 2 includes the following steps:
[0067] Step 2.1: Construct a local extremum filter with a kernel of 3;
[0068] Step 2.2: Use the local extremum filter grayscale image I to filter to obtain all maximum and minimum points that meet the conditions;
[0069] The details of obtaining all the maximum and minimum points satisfying the conditions in step 2.2 are as follows:
[0070] 1) In the k*k neighborhood centered on pixel p, if at most k-1 elements ar...
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