Deep learning techniques are widely implemented in computer vision applications. The Convolutional Neural Networks (CNN) is a deep learning class that is the most effective in categorizing the statistical characteristics of images. It is often a challenging task to classify the frequency level region in various low-resolution image. In this research, we proposed the CNN for classification of gradient profile priors by learning on several gradient characteristics such as horizontal gradient acceleration, vertical gradient acceleration, the Relational Gradient Direction and Edge Sketch Image. This technique is used multiple building blocks to designed features through backpropagation with automatic and adaptive spatial hierarchies learning. The performance comparison was improved in classification of the frequency level area in various low-resolution image input that was illustrated in the experimental results which evaluate with several predictive and conventional classification techniques.
This paper presents a study of neuro-fuzzy behavior in clustering gradient profile spectral characteristics. Various types of image scene are chosen to evaluate neuro-fuzzy performance. The combinations of training data subsets are learned by ANFIS model to generate gradient profile priors, which are used as optimum weight selection criteria for image enhancement. The experimental results illustrate quantitative performance improvement and perceptual improvement in recovery of the high-resolution details in various images.
In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.
