The significant increase in drug abuse cases prompts developers to investigate techniques that mimic the hallucinations imagined by addicts and abusers, in addition to the increasing demand for the use of decorative images resulting from the use of computer technologies. This research uses Deep Dream and Neural Style Transfer technologies to solve this problem. Despite the significance researches on Deep Dream technology, there are several limitations in existing studies, including image quality and evaluation metrics. We have successfully addressed these issues by improving image quality and diversifying the types of generated images. This enhancement allows for more effective use of Deep Dream in simulating hallucinated images. Moreover, the high-quality generated images can be saved for dataset enlargement, like the augmentation process. Our proposed deepy-dream model combines features from five convolutional neural network architectures: VGG16, VGG19, Inception v3, Inception-ResNet-v2, and Xception. Additionally, we generate Deep Dream images by implementing each architecture as a separate Deep Dream model. We have employed autoencoder Deep Dream model as another method. To evaluate the performance of our models, we utilize normalized cross-correlation and structural similarity indexes as metrics. The values obtained for those two quality measures for our proposed deepy-dream model are 0.1863 and 0.0856, respectively, indicating effective performance. When considering the content image, the metrics yield values of 0.8119 and 0.3097, respectively. Whiefor the style image, the corresponding quality measure values are 0.0007 and 0.0073, respectively.

The deep dream is one of the most recent techniques in deep learning. It is used in many applications, such as decorating and modifying images with motifs and simulating the patients' hallucinations. This study presents a deep dream model that generates deep dream images using a convolutional neural network (CNN). Firstly, we survey the layers of each block in the network, then choose the required layers, and extract their features to maximize it. This process repeats several iterations as needed, computes the total loss, and extracts the final deep dream images. We apply this operation on different layers two times; the former is on the low-level layers, and the latter is on the high-level layers. The results of applying this operation are different, where the resulting image from applying deep dream on the high-level layers are clearer than those resulting from low-level layers. Also, the loss of the images of low-level layers ranges between 31.1435 and 31.1435, while the loss of the images of upper layers ranges between 20.0704 and 32.1625.

In recent years, deep dream and neural style transfer emerged as hot topics in deep learning. Hence, mixing those two techniques support the art and enhance the images that simulate hallucinations among psychiatric patients and drug addicts. In this study, our model combines deep dream and neural style transfer (NST) to produce a new image that combines the two technologies. VGG-19 and Inception v3 pre-trained networks are used for NST and deep dream, respectively. Gram matrix is a vital process for style transfer. The loss is minimized in style transfer while maximized in a deep dream using gradient descent for the first case and gradient ascent for the second. We found that different image produces different loss values depending on the degree of clarity of that images. Distorted images have higher loss values in NST and lower loss values with deep dreams. The opposite happened for the clear images that did not contain mixed lines, circles, colors, or other shapes.

Recently, Deep Learning (DL) has been used in a new technology known as the Deep Dream (DD) to produce images that resemble dreams. It is utilized to mimic hallucinations that drug users or people with schizophrenia experience. Additionally, DD is sometimes incorporated into the images as decoration. This study produces DD images using two deep-CNN model architectures (Inception-ResNet-V2 and Inception-v3). It starts by choosing particular layers in each model (from both lower and upper layers) to maximize their activation function, then detect several iterations. In each iteration, the gradient is computed and then used to compute loss and present the resulting images. Finally, the total loss is presented, and the final deep dream image is visualized. The output of the two models is different, and even for the same model there are some variations, the lower layers' loss values in the Inception-v3 model are significantly higher in comparison to the upper levels' values. In the case of Inception-ResNet-V2, the loss values are convergent.

Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely used in the artificial intelligence (AI) fields. This paper is the first systematic review of DD. We focused on the definition, importance, background, and applications of DD. Natural language processing (NLP), images, videos, and audio are the main fields in which DD is applied. We also discussed the main concepts of the DD, like transfer learning and Inception. We addressed the contributions, databases, and techniques that have been used to build the models, the limitations, and evaluation metrics for each one of the included research papers. Finally, some interesting recommendations have been listed to serve the researchers in the future. Index Terms— Deep dream, deep CNN, gradient ascent, Inception, style transfer.

This work presents a primary dataset collected from various geographic locations in Iraq for the seedlings of eight varieties of grapes that are used for local consumption and export. Grape types included in the dataset are: deas al-annz, kamali, halawani, thompson seedless, aswud balad, riasi, frinsi, shdah. Leaves of each type of the seasoned fruit were photographed with high resolution device. A total of 8000 images (i.e., 1000 images per category) were captured using random sampling approach while maintaining the balance and diversity within grape image data. The proposed dataset is of significant potential impact and usefulness with features including (but not limited to) 8 varieties, that have different tastes and can support various industry in agriculture and food manufactures.