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The research on crop phenotype is one of the important measures in crop breeding. Crop breeding is a technology on selecting the good seeds by the crop phenotype. The farm remote sensing image analysis is a simple and effective method for rapid analysis of crop phenotype. However, the farm remote sensing image acquired by UAV (unmanned aerial vehicle) will be affected by the noise. In order to process and analyze the remote sensing image accurately, the remote sensing image should be first denoised. Typical image denoising algorithm of frequency domain is wavelet threshold algorithm. Wavelet transform can identify the singular signal accurately; however, the traditional wavelet can't effectively deal with multidimensional signal. For example, two-dimensional wavelet obtained from one-dimensional wavelet tensor only has horizontal and vertical directions, and the wavelet filter is isotropic, which is not sensitive to the image with more directions of the texture; while using the traditional wavelet threshold denoising algorithm to handle noise image, it is easy to make texture region blurry compared with the common wavelets. It is well known that the shear transformation has been introduced into the definition of the shearlet, which makes shearlet can represent more directions. In order to capture the directions of local geometric features of image, shearlet must be compactly suppressed. Anisotropic expansion scale transform can be applied in multi-resolution analysis. From coarse scale to fine scale approximation signal, namely in the approximation image in smooth regions using coarse scale, and in the approximation image with directions of texture using fine scale, the optimal approximation of singular curves can be achieved through the application of multi-resolution analysis method. Shearlet filter is anisotropic, and it is very sensitive to the direction of the texture; however, with the increase of noise standard deviation, after denoising, using shearlet algorithm generates artifacts easily. In brief, the denoising algorithm based on the total variation model has the advantage of edge preserving and noise reduction. In the application of shearlet for image denoising, we find that the shearlet with noise in the texture region can detect the texture direction, and can effectively remove the noise; but when the noise standard deviation is large, the application of shearlet algorithm in the image can cause the denoised image to tend to produce some artifacts, which will be identified as noise texture perhaps due to the shear wave in the smooth region. In order to solve the problem of discrete shearlet algorithm, this paper proposes a method based on the combination of shearlet and total variation model to eliminate the artifacts. First, select the generated Symmlet quadrature mirror filter, and ascertain anisotropic scale parameters and shear parameters. Perform discrete shearlet transformation to farmland remote sensing image with multi-resolution analysis. Wavelet coefficients can be obtained using the conventional method by anisotropic wavelet transformation. Wavelet coefficients are projected into wavelet of image. Hard threshold algorithm is used to handle noise coefficients. Wavelet coefficients are adopted to reconstruct farm remote sensing image. After using total variation model to smooth artifacts, the mirror extension is used to preprocess the image. In part of the experiment, the algorithm proposed in this paper is compared with the shearlet algorithm and total variation algorithm, and this algorithm gets a PSNR (peak signal to noise ratio) 1dB more than that from total variation model, and an iteration number of total variation less than that from direct total variation. In addition, not only can this algorithm effectively denoise farm remote sensing image noise and visual effects, but also can remove artifact better than the discrete shearlet transform algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]