Abstract
Affected by special underground circumstances of coal mine, the image clarity of most images captured in the mine is not very high, and a large amount of image noise is mingled with the images, which brings further downhole images processing many difficulties. Traditional image denoising method easily leads to blurred images, and the denoising effect is not very satisfactory. Aimed at the image characteristics of low image illumination and large amount of noise and based on the characteristics of color detail blindness and simultaneous contrast of human visual perception, this paper proposes a new method for image denoising based on visual characteristics. The method uses CIELab uniform color space to dynamically and adaptively decide the filter weights, thereby reducing the damage to the image contour edges and other details, so that the denoised image can have a higher clarity. Experimental results show that this method has a brilliant denoising effect and can significantly improve the subjective and objective picture quality of downhole images.
Highlights
The application environment in the coal industry is always special, and downhole images are always mingled with large amount of image noise interfered by complex underground environment, mechanical vibration, and dust noise
With the gradual deepening of the various branches of mathematics in the theory and applications, great progress in image denoising technology has been achieved in terms of mathematical morphology, partial differential equations, genetic algorithms, information theory, and so forth, producing a number of new denoising algorithms [5,6,7,8], including denoising algorithm based on mathematical morphology [9,10,11,12], denoising algorithm based on fuzzy theory [13, 14], denoising algorithm based on genetic algorithms [15], neural network-based denoising algorithm [16], and denoising algorithm based on information entropy
Denoising processing on underground images has been conducted in this paper based on CIELab color space and CIELab chromatism aberration computational formula
Summary
The application environment in the coal industry is always special, and downhole images are always mingled with large amount of image noise interfered by complex underground environment, mechanical vibration, and dust noise. This brings many difficulties for the subsequent processing of the image. The mean denoising is suitable for removing grain noise in images, but the images always become blur because this method is too average; median denoising is good for removing impulse noise in the image, but the denoising effect is not very ideal when the noise area inside the window is too large; Wiener filter is suitable for removing the white. The aforementioned are the current research status of image denoising; with the combination of noise characteristics of the coal mine, finding a method that can preserve the image detail and textural features while at the same time reducing image noise has become the research goal of this paper
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