Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.

The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively

Remote sensing imagery super-resolution (SR) has gained focus due to its importance in enhancing satellite images to aid in the study of Earth. Recently, deep learning has advanced significantly in SR, particularly in single-image super-resolution (SISR). However, SISR still struggles with challenges like atmospheric occlusion and sensor noise, which degrade image quality. In this paper, a novel hybrid dense attention network (HyDA-Net) is proposed that highlights the idea of multi-image super-resolution (MISR) to address SISR problems. HyDA-Net is a three-branch architecture that emphasizes the importance of information compensation and the correlation between multiple images of the same scene by fusing 3D and 2D features. HyDA-Net introduces a novel 3D Dense Attention Block (3D-DAB) designed to improve the preservation of fine details in satellite images. 3D-DAB integrates a 3D dense block and a tailored feature attention mechanism with a 3D convolution to skillfully capture high-frequency details of dense features from multiple low-resolution satellite images that will complement the low-frequency components. Additionally, 3D-DAB has a global residual connection and multi-level local residual connections inside 3D dense block to avoid the vanishing gradient problem during training. Extensive experiments using real-captured satellite datasets, namely PROBA-V and MuS2, show that HyDA-Net outperforms state-of-the-art models in different spectral bands. Moreover, a cross-dataset experiment is conducted to further evaluate the robustness and generalizability of the proposed HyDA-Net.