In recent years, frameworks that employ Generative Adversarial Networks (GANs) have achieved immense results for various applications in many fields especially those related to image generation both due to their ability to create highly realistic and sharp images as well as train on huge data sets. However, successfully training GANs are notoriously difficult task in case ifhigh resolution images are required. In this article, we discuss five applicable and fascinating areas for image synthesis based on the state-of-theart GANs techniques including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Manipulation, 3D Image Synthesis and DeepMasterPrints. We provide a detailed review of current GANs-based image generation models with their advantages and disadvantages.The results of the publications in each section show the GANs based algorithmsAREgrowing fast and their constant improvement, whether in the same field or in others, will solve complicated image generation tasks in the future.

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In the past few years, Generative Adversarial Networks (GANs) have received immense attention by researchers in a variety of application domains. This new field of deep learning has been growing rapidly and has provided a way to learn deep representations without extensive use of annotated training data. Their achievements may be used in a variety of applications, including speech synthesis, image and video generation, semantic image editing, and style transfer. Image synthesis is an important component of expert systems and it attracted much attention since the introduction of GANs. However, GANs are known to be difficult to train especially when they try to generate high resolution images. This paper gives a thorough overview of the state-of-the-art GANs-based approaches in four applicable areas of image generation including Text-to-Image-Synthesis, Image-to-Image-Translation, Face Aging, and 3D Image Synthesis. Experimental results show state-of-the-art performance using GANs compared to traditional approaches in the fields of image processing and machine vision.

In the past decade, dietary assessment has been one of the most popular topics of research in the food industry, which has resulted in developing several automatic or semi-automatic dietary assessment systems using visible spectrum images for food recognition. However, the main shortcoming of visible spectrum image-based systems is its inability to differentiate foods of similar color. Researchers have added additional features such as shape, size and texture to the color model to improve the overall accuracy. However, the shape and size features are rendered inefficient when recognizing food in the mixed or cooked form. The aim of this research is to show the capability of hyperspectral bands for accurate food recognition based on individual spectral bands. In this work we use a hyperspectral imaging system of 240 spectral bands with the wavelength range between 400 nm to 900 nm. The ReliefF and PCA methods select/extract less, but the most informative features which are important to learn Logistic Regression and Support Vector Machine (SVM) as binary and multiple classifiers, respectively. A total of 20 different food samples in various forms (uncut and cut), shapes, and sizes were used in this study. The prediction results indicate that the hyperspectral images have the advantages of being able to recognize different food items from a mixed form with similar color and similar food types with different colors. In our experiments the highest classification accuracy of 0.6874 with 20 different food samples is produced by SVM multiclassification of ReliefF data with the top 110 hyperspectral features.We are able to obtain approximately 0.90 accuracy, using binary classification on a specific subset of food samples.

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