Denoising for locust slice image with texture preserving based on coupling technology of variational method and shearlet transform

Abstract: Image processing and analysis play the key roles in the smart agriculture system. As the agricultural images are often taken in the open air, the heavy atmospheric haze and dust in the air are often included in the images. It's very difficult to differentiate the noises and the detail texture in the images using existing common methods. As the detail texture is often employed to identify the important feature of different plants, the texture preserving image denoising algorithm plays an important role in the agricultural field. The existed methods can't identify the detail texture and the noise, and so it is difficult to fulfill the texture preserving image denoising. The shearlet transform proposed in recent years possesses the multi-scale directivity as the shear matrix is introduced into it. So the shearlet transform can be used to describe the detail texture precisely. However, the shortcoming of the shearlet transform is that it often mistakes the noise as the texture in many cases. On the contrary, the variational method views all the objects in the images as the smooth domains, this results in detail texture in the images being often destroyed. To solve the problem, we proposed a coupling technology of the multi-scale variational method based on the interpolation wavelet frame and the shearlet transform, in which the variational method was employed to identify the contour and the shearlet transform to describe the texture precisely. According to this method, the image was firstly decomposed and reconstructed by means of the shearlet transform, which can remove most of the noise in the image. Second, the variational method based on the multi-scale interpolative wavelet frame was employed to smooth the denoised image. This can divide the image into some domains, and they possessed different texture feature which could be identified by means of the correlation value derived from the gray-level co-occurrence matrix of the grayscale image. Compared to the variational method under the total variation (TV) frame, the multi-scale interpolative wavelet frame can identify more detail domains which was helpful to improve the quality of the denoised images. To the image noised by the Gauss noise with the standard derivation 10, the peak signal to noise ratio (PSNR) of the denoised image obtained by the variational method based on the multi-scale interpolative wavelet frame was 1.3131 larger than PSRN obtained by one based on the TV frame, and the structural similarity image measurement (SSIM) increased by 4.5% accordingly. Next, the variational method and the shearlet transform method can be used to remove the noises existed in the cartoon region and the texture region, respectively. This can overcome the shortcomings of only one method and remain all the merits in all the denoising algorithms. For instance, median filtering can be applied to remove the noise in the smooth domain, and the shearlet transform can be employed to remove the noise in the domain with abundant textures. Last, the locust slice images were taken as the examples to illustrate the proposed method. The numerical experiments showed that coupling technology proposed in this paper can improve the image denoising precision effectively compared to each single method. The value of the PSNR was improved by 6.37% compared to the multi-scale variational method, and by 5.90% compared to the shearlet transform method. Compared with another attractive algorithm K-SVD which is an excellent algorithm for designing of overcomplete dictionaries for sparse representation, the coupling technology also had apparent superiority. To the locust slice images noised by the Gauss noise with the standard derivation 40, PSRN obtained by the coupling technology increased by 0.43 compared to the K-SVD algorithm, and SSIM was improved by 2.5% accordingly. In addition, the coupling technology was independent of the images, and the K-SVD algorithm yielded sparse representation for the training signals which limits its application to some extent.