Abstract
Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.
Highlights
There is a very large dynamic range in real scenes, and it has a very rich color and light information the human eye can partly observe the dynamic range of real scenes
To improve the image recognition rate and recognition efficiency, we propose optimization methods for a high-dynamic range image edge detail enhancement algorithm
The first-order differential, which is often used in the dynamic image processing, is called the gradient
Summary
There is a very large dynamic range in real scenes, and it has a very rich color and light information the human eye can partly observe the dynamic range of real scenes. Document [1] proposes an image enhancement algorithm based on a dual-domain decomposition that simultaneously achieves the image contrast improvement with noise suppression. The above image enhancement method has a poor data transmission and conversion effect in the practical application process, leading to the degradation of image details, and the low image recognition rate and recognition efficiency. To improve the image recognition rate and recognition efficiency, we propose optimization methods for a high-dynamic range image edge detail enhancement algorithm. The innovation point of this article is to enhance the process of image edge details, based on the fuzzy dynamic image and the reverse difference calculation, remove the noise in the image data, improve the gray value of the dynamic image, facilitate the subsequent recognition work, and improve the recognition speed.
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