A flexible representation of quantum images (FRQI) was proposed to facilitate the extension of classical (non-quantum)-like image processing applications to the quantum computing domain. The representation encodes a quantum image in the form of a normalized state, which captures information about colors and their corresponding positions in the images. Since its conception, a handful of processing transformations have been formulated, among which are the geometric transformations on quantum images (GTQI) and the CTQI that are focused on the color information of the images. In addition, extensions and applications of FRQI representation, such as multi-channel representation for quantum images (MCQI), quantum image data searching, watermarking strategies for quantum images, a framework to produce movies on quantum computers and a blueprint for quantum video encryption and decryption have also been suggested. These proposals extend classical-like image and video processing applications to the quantum computing domain and offer a significant speed-up with low computational resources in comparison to performing the same tasks on traditional computing devices. Each of the algorithms and the mathematical foundations for their execution were simulated using classical computing resources, and their results were analyzed alongside other classical computing equivalents. The work presented in this review is intended to serve as the epitome of advances made in FRQI quantum image processing over the past five years and to simulate further interest geared towards the realization of some secure and efficient image and video processing applications on quantum computers.

Semantic segmentation of high-resolution remote sensing images has emerged as one of the foci of research in the remote sensing field, which can accurately identify objects on the ground and determine their localization. In contrast, the traditional deep learning-based semantic segmentation, on the other hand, requires a large amount of annotated data, which is unsuitable for high-resolution remote sensing tasks with limited resources. It is therefore important to build a semantic segmentation method for high-resolution remote sensing images. In this paper, it is proposed an improved U-Net model based on transfer learning to solve the semantic segmentation problem of high-resolution remote sensing images. The model is based on the symmetric encoder–decoder structure of U-Net. For the encoder, transfer learning is applied and VGG16 is used as the backbone of the feature extraction network, and in the decoder, after upsampling using bilinear interpolation, it is performed multiscale fusion with the feature maps of the corresponding layers of the encoder in turn and is finally obtained the predicted value of each pixel to achieve precise localization. To verify the efficacy of the proposed network, experiments are performed on the ISPRS Vaihingen dataset. The experiments show that the applied method has achieved high-quality semantic segmentation results on the high-resolution remote sensing dataset, and the MIoU is 1.70%, 2.20%, and 2.33% higher on the training, validation, and test sets, respectively, and the IoU is 4.26%, 6.89%, and 5.44% higher for the automotive category compared to the traditional U-Net.

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent ability of automatic feature extraction. Most existing methods for medical image denoising adapted to certain types of noise have difficulties in handling spatially varying noise; meanwhile, image detail losses and structure changes occurred in the denoised image. Considering image context perception and structure preserving, this paper firstly introduces a medical image denoising method based on conditional generative adversarial network (CGAN) for various unknown noises. In the proposed architecture, noise image with the corresponding gradient image is merged as network conditional information, which enhances the contrast between the original signal and noise according to the structural specificity. A novel generator with residual dense blocks makes full use of the relationship among convolutional layers to explore image context. Furthermore, the reconstruction loss and WGAN loss are combined as the objective loss function to ensure the consistency of denoised image and real image. A series of experiments for medical image denoising are conducted with the denoising results of and on JSRT datasets and and on LIDC datasets. Compared with the state-of-the-art methods, the superior performance of the proposed method is outstanding.

Deep learning techniques have provided new research methods for computer-aided diagnosis, allowing researchers to use deep learning methods to process medical imaging data. Chest X-ray examinations are widely used as a primary screening method for chest diseases. Therefore, it is of great importance to study diagnosis of 14 common pathologies in chest X-ray images using deep learning methods. In this paper, we propose a deep learning model named AM_DenseNet for chest X-ray image classification. The model adopts a dense connection network and adds an attention module after each dense block to optimize the model’s ability to extract features, and finally a Focal Loss function is applied to solve the data imbalance problem. The experiments used chest X-ray images as model input and were trained to output the probabilities of 14 chest pathologies. The Area under the ROC curve (AUC) was used to measure the classification results, and the final average AUC was 0.8537. The experimental results show that the AM_DenseNet model could complete the pathology classification of the chest X-ray images effectively.