Objective

2

Harsh environments exist in the process of medical image acquisition and video surveillance. Such environments lead to poor image quality and many strong noise spots

which in turn affect the doctor's identification of lesions. When dealing with strong noise images

traditional variational model-based algorithms are computationally complex and have slow convergence because they need to calculate high-order partial differential equations. The curvature filtering model that implicitly uses image curvature information deals with strong noise images

and incomplete denoising defects occur. An improved curvature filtering algorithm is proposed in this study to overcome these deficiencies and achieve strong noise removal from images while maintaining the edge and detail features of the images and realizing a fast denoising process.

Method

2

This study proposes a novel algorithm for strong noise denoising based on improved curvature filters. The curvature is implicitly calculated

such that the proposed algorithm is as fast as the curvature filtering algorithm. The semi-window triangular tangent plane is combined with the minimum trigonometric tangent plane projection operator to replace the only minimum triangulation tangent plane projection operator for traditional curvature filtering

thereby enhancing the denoising capability of the algorithm aiming for strong noise images. According to the characteristics of strong noise spots in strong noise images

the regular energy function is modified

and the regular energy of local variance is added. These actions result in a reasonable constraint of regular terms and improves the denoising performance of the algorithm

thus enhancing the denoising capability and protecting the image edges.

Result

2

An improved curvature filtering algorithm is proposed to denoise strong noise images. Images are reconstructed based on the Gaussian curvature and local variance. Denoising performance is improved

particularly in processing images with high noise.The performance of the proposed algorithm is tested against mixed noise images of different intensities to verify its effect and texture-preserving capability. For instance

0.1-density salt-and-pepper-mixed Gaussian noise with a standard deviation of 50 is added to the classic image of Lena and an emphysema CT lung image in the experiment. The noise reduction effects are compared with those of the traditional algorithms based on the denoising algorithm (Rudin-Osher-Fatemi model

ROF) and curvature filtering and denoising. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used as objective evaluation indicators of the performance of the filtering algorithm. The proposed algorithm can effectively preserve the edge and detail features of images when denoising high-noise images and has good PSNR and SSIM. For the mixed salt-and-pepper noise density of 0.02 and Gaussian noise variance of 30 compared with the ROF model and the curvature filtering algorithm

the PSNR can be improved by 0.196 dB and 2.264 dB

respectively

and the SSIM can increase by 0.13 and 0.305

respectively. Especially in strong noise environments

such as mixed salt-and-pepper noise density of 0.1

Gaussian noise variance of 50

and superimposed Poisson noise

experimental results show that the PSNR obtained by the proposed algorithm increases by 2.196 dB more than the denoising image of the ROF model and by 3.194 dB more than the curvature filtering algorithm. The SSIM obtained by the proposed algorithm is 0.398 more than that of the ROF model and 0.403 more than that of the curvature filtering model. The traditional ROF model can achieve a good denoising image

but it requires 26 s of running time and thereby cannot achieve real-time denoising. Although curvature filtering can acquire denoising images in a relatively short time

its denoising capability is insufficient and noise cannot be removed efficiently. Implicitly calculated image curvature is adopted

and the processing speed of the algorithm is similar to that of the curvature filtering algorithm.

Conclusion

2

The curvature filtering algorithm is optimized based on the noise characteristics of strong noise images. According to the enhanced denoising capability projection operator and the modified energy function regularization term

the curvature filtering model can be better applied to strong noise images than traditional methods. Experiments are performed on images with three mixed strong noise levels. The corresponding running time

PSNR

and SSIM are calculated for comparison. The PSNR

SSIM

and image visual effect of the proposed method are superior to those of the total variation(TV) and adaptive fidelity term total variation (AFTV) models

particularly for the images polluted by strong noise. The running time of the proposed algorithm is close to that of the curvature filtering algorithm and is significantly faster than that of the ROF model. Experimental results show that compared with the traditional variational method

the proposed algorithm has a significant effect on denoising strong noise images.