For agricultural disease image identification, obtained images are typically unclear, which can lead to poor identification results in real production environments. The quality of an image has a significant impact on the identification accuracy of pre-trained image classifiers. To address this problem, we propose a generative adversarial network with dual-attention and topology-fusion mechanisms called DATFGAN. This network can effectively transform unclear images into clear and high-resolution images. Additionally, the weight sharing scheme in our proposed network can significantly reduce the number of parameters. Experimental results demonstrate that DATFGAN yields more visually pleasing results than state-of-the-art methods. Additionally, treated images are evaluated based on identification tasks. The results demonstrate that the proposed method significantly outperforms other methods and is sufficiently robust for practical use.

The growth of the most significant field crops such as rice, wheat, maize, and soybean are influenced because of various pests. And crop production is decreased due to various categories of insects. Deep learning technologies significantly increased the efficiency of identifying and controlling agricultural pests attack. However, agricultural pests images obtained are often obscure and unclear because of the sparse density of cameras deployed in the real farmland. This always makes pests difficult to recognize and monitor. Additionally, the existing classification and segmentation methods are not satisfying for the identification of low-resolution images because they are pre-trained on the clear and high-resolution datasets. Therefore, it is crucial to restore and upscale the obtained low-resolution pest images in order to improve classification accuracy and the recall rate of the instance segmentation. In this paper, we propose a generative adversarial network (GAN) with quadra-attention and residual and dense fusion mechanisms to transform low-resolution pest images. Compared with previous state-of-the-art PSNR-oriented super-resolution methods, our proposed method is more powerful in image reconstruction and achieves the state of the art performance. The experiment results show that after reconstructing with our proposed gan, the recall rate increased by 182.89% and classification accuracy also improved a lot. Besides, our proposed method could decrease the density of the camera layout in the agricultural Internet of Things (IOT) monitor systems and the cost of infrastructure, which is practical for real-world applications.

Structured illumination microscopy (SIM) is an important super-resolution-based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low-light and short-exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this article, we propose a single-frame SIM (SF-SIM) based on deep learning. Our SF-SIM only needs one shot of a structured illumination frame and generates similar results compared with the traditional SIM systems that typically require 15 shots. In our SF-SIM, we propose a noise estimator that can effectively suppress the noise in the image and enable our method to work in the low-light and short-exposure environment without the need for stacking multiple frames for nonlocal denoising. We also design a bandpass attention module that makes our deep network more sensitive to the change of frequency and enhances the imaging quality. Our proposed SF-SIM is almost 14 times faster than traditional SIM methods when achieving similar results. Therefore, our method is significantly valuable for the development of microbiology and medicine.

Image datasets acquired from orchards are commonly characterized by intricate backgrounds and an imbalanced distribution of disease categories, resulting in suboptimal recognition outcomes when attempting to identify apple leaf diseases. In this regard, we propose a novel apple leaf disease recognition model, named RFCA ResNet, equipped with a dual attention mechanism and multi-scale feature extraction capacity, to more effectively tackle these issues. The dual attention mechanism incorporated into RFCA ResNet is a potent tool for mitigating the detrimental effects of complex backdrops on recognition outcomes. Additionally, by utilizing the class balance technique in conjunction with focal loss, the adverse effects of an unbalanced dataset on classification accuracy can be effectively minimized. The RFB module enables us to expand the receptive field and achieve multi-scale feature extraction, both of which are critical for the superior performance of RFCA ResNet. Experimental results demonstrate that RFCA ResNet significantly outperforms the standard CNN network model, exhibiting marked improvements of 89.61%, 56.66%, 72.76%, and 58.77% in terms of accuracy rate, precision rate, recall rate, and F1 score, respectively. It is better than other approaches, performs well in generalization, and has some theoretical relevance and practical value.

Moiré patterns arise when two repetitive textures are superimposed, which often degrade the image quality when taking photos. However, controllable moiré patterns can be useful in many practical applications e.g. image hiding. Existing moiré-based approaches heavily rely on manually designed filters, resulting in slow and inflexibility. Moreover, existing methods often struggle to recover image details, making it challenging to achieve satisfactory visual quality. To address these limitations, in this study, a deep moiré hiding network (DMHN) is proposed to control the generation of moiré patterns and applied to the image hiding task. The network encodes the secret image to generate modulation parameters of moiré patterns and hide the images in unreadable gratings. When superimposing the hidden image with the key grating, the hidden image becomes visible again in the form of moiré patterns. To better restore the moiré-based reappeared images, a grating removal network (GRN) is also presented to eliminate the residual gratings and recover high-frequency image details. Experiments demonstrate that the proposed method can effectively hide and restore the images via moiré modulation and generation.