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Because of the complex environment in coal mines, it is extremely hard to process pulverized coal images with large sum of noise. Fractional dimension characterization and model in bounded variation space are presented to optimize pulverized coal image. This paper puts on fractional calculus theory in bounded variation space and discusses the characterization model to enhance image optimization stability. Applying the regional characteristics of the pulverized coal image, the fractional dimension characteristic parameter u and regularized parameter λ of each image pixels are modified by adaptive algorithm. To evaluate image optimization effect, Numeric experimentations show that the PSNR (peak signal-to-noise ratio) and ERI (edge-preserving index). The two quantitative indicators have better performance considerably with the improved algorithm than the traditional algorithm. Because of the sound smoothing effect in the “non-texture region” and the excellent texture retention capacity in the “texture region”, fractional dimension characterization in bounded variation space optimization algorithm is effective and expeditious image filtering approach. The advanced algorithm is employed to observe pulverized coal in underground and succeeds in achieving satisfactory outcomes.
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