An Analysis of Image Enhancement Techniques


An Analysis of Image Enhancement Techniques – The problem of image enhancement using deep reinforcement learning (RL) is of great interest in computer vision and in various scientific field, as it is the most important part of deep reinforcement learning (RL). In this paper, we propose a framework which leverages RL to perform image restoration and generate a new set of images. For our research, we have conducted extensive experiments on four datasets. We achieve an average of 3.6 images in 4 hours on the UCI dataset. This task is challenging for most of RL systems such as this one, as the training is typically conducted by hand and does not require a machine. This is also why we are proposing a novel method to extract a new set of images from the input image without manual annotation. We have developed a deep RL system to generate images for a new set of subjects through this method. The system trained on all subjects has been made publicly available.

We propose a novel approach for the problem of face recognition with text. Using image-labeled data for face recognition, the image-based learning is divided into two stages: (1) an unsupervised learning based on deep convolutional layer, where the image labels are learned in an objective setting for training the layer, (2) a supervised learning based on a multilinear dictionary learning algorithm. We train a learning algorithm to optimize the weights of the learned dictionary and propose an efficient method to learn the labels in a unified way using the image-labeled data. We use multi-task neural network for all training data and compare the performance of our supervised learning based algorithm with the well known CNN-CNN neural network for face recognition task. Experiments show that our approach is able to achieve comparable or better performance than recent state-of-the-art face recognition methods on both VGG and MNIST datasets.

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An Analysis of Image Enhancement Techniques

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    Multi-task Facial Keypoint Prediction with Densely Particular TextualsWe propose a novel approach for the problem of face recognition with text. Using image-labeled data for face recognition, the image-based learning is divided into two stages: (1) an unsupervised learning based on deep convolutional layer, where the image labels are learned in an objective setting for training the layer, (2) a supervised learning based on a multilinear dictionary learning algorithm. We train a learning algorithm to optimize the weights of the learned dictionary and propose an efficient method to learn the labels in a unified way using the image-labeled data. We use multi-task neural network for all training data and compare the performance of our supervised learning based algorithm with the well known CNN-CNN neural network for face recognition task. Experiments show that our approach is able to achieve comparable or better performance than recent state-of-the-art face recognition methods on both VGG and MNIST datasets.


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