IMAGE SEQUENCE SUPER-RESOLUTION BASED ON LEARNING USING FEATURE DESCRIPTORS

Abstract: There is currently a growing demand for high-resolution images and videos in several domains of knowledge, such as surveillance, remote sensing, medicine, industrial automation, microscopy, among others. High res-olution images provide details that are important to tasks of analysis and visualization of data present in the images. However, due to the cost of high precision sensors and the limitations that exist for reducing the size of the image pixels in the sensor itself, high-resolution images have been acquired from super-resolution meth-ods. This work proposes a method for super-resolving a sequence of images from the compensation residual learned by the features extracted in the residual image and the training set. The results are compared with some methods available in the literature. Quantitative and qualitative measures are used to compare the results obtained with super-resolution techniques considered in the experiments.