The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Liviidae), is a citrus pest that vectors the bacterium that causes huanglongbing (HLB) disease between citrus trees. It has become a very large problem to the US citrus growers. Male ACP find females by vibrating the substrate (branch) to call them. The females vibrate a response and the males track these responses to find them in a citrus tree. We have created three ACP call recognition systems: one using Matlab, one using TensorFlow implemented on a Raspberry Pi, and one using Edge Impulse implemented on a RP2040 microcontroller. All three systems recognized calls with an accuracy greater than 79.5%. A demonstration on a single, long recording of two ACP vibrating to each other using the RP2040 system shows that it would be useful in a live implementation.
A convolutional capsule network that characterizes small objects previously has shown remarkable performance in small object classification. We extend the idea of capsules to the image denoising task and combine it with the generative adversarial network to develop a generative adversarial capsule network (DeCapsGAN). Both the generator and discriminator adopt the capsule network architecture. The convolutional capsule network is used to capture richer image features. We introduce deconvolution into the generator and propose a convolutional–deconvolutional capsule block. Skip connections are beneficial to transfer image features to deeper networks. A pretrained residual network (ResNet) is implemented as a feature extractor that captures features from the denoised image and reference image to measure the difference in perceptual information in the feature space. The performance of the proposed model is evaluated on the image with synthetic noise (Gaussian noise and mixed Gaussian with impulse noise) and real noise. Extensive experiments show that our model achieves excellent denoising performance in terms of both visual quality and quantitative metrics.
Image prior and sparse coding learning methods have important uses in image denoising. Many denoising methods learn priors either from the noisy image itself or an external clean image dataset. But using only these as priors does not always reconstruct the image effectively. In addition, when the image is corrupted by noise, the local sparse coding coefficient obtained from a noisy image patch is inaccurate, restricting denoising performance. We present a noise removal framework based on external prior learning and an internal mean sparse coding method, making use of the innate sparsity and nonlocal self-similarity (NSS) of natural images. Specifically, we first obtain external priors from a clean natural image dataset by Gaussian mixture model. The external priors are applied to guide the subspace clustering of internal noisy image patches, and a compact dictionary is generated for each internal noisy patch cluster. Then an internal mean sparse coding strategy based on NSS is introduced into the sparse representation model, whose regularization parameters then are deduced through a Bayesian framework. An iterative shrinkage method is employed to solve the l1-optimization problem in the sparse representation model. Application of the noise removal model to 16 test images demonstrates denoising performance exceeding other competing methods.
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