Learning-based super-resolution image reconstruction on multi-core processor

Wang

et al. 2019

Sensors

In the case of space-based space surveillance (SBSS), images of the target space objects captured by space-based imaging sensors usually suffer from low spatial resolution due to the extremely long distance between the target and the imaging sensor. Image super-resolution is an effective data processing operation to get informative high resolution images. In this paper, we comparably study four recent popular models for single image super-resolution based on convolutional neural networks (CNNs) with the purpose of space applications. We specially fine-tune the super-resolution models designed for natural images using simulated images of space objects, and test the performance of different CNN-based models in different conditions that are mainly considered for SBSS. Experimental results show the advantages and drawbacks of these models, which could be helpful for the choice of proper CNN-based super-resolution method to deal with image data of space objects.

Section: Introductionmentioning

confidence: 99%

A Comparable Study of CNN-Based Single Image Super-Resolution for Space-Based Imaging Sensors

Wang

et al. 2019

Sensors

Colorimetry and Image Processing

“…Although several methods have been implemented in this field, there are still several future open research challenges [1]. SR can be found in fields like surveillance, remote sensing, astronomy, and an extensive set of consumer electronics applications, among many others [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Color Reconstruction and Resolution Enhancement Using Super-Resolution

Quevedo¹,

Callicó²

2018

Image super-resolution (SR) is a process that enhances the resolution of an image or a set of images beyond the resolution of the imaging sensor. Although there are several superresolution methods, fusion super-resolution techniques are well suited for real-time implementations. In fusion super-resolution, the high-resolution images are reconstructed using different low-resolution-observed images, thereby increasing the high-frequency information and decreasing the degradation caused by the low-resolution sampling process. In terms of color reconstruction, standard reconstruction algorithms usually perform a bilinear interpolation of each color. This reconstruction performs a strong low-pass filtering, removing most of the aliasing present in the luminance signal. In this chapter, a novel way of color reconstruction is presented by using super-resolution in order to reconstruct the missing colors.

Recent Advances in Image and Video Coding

“…Even though numerous methods have been developed to this end, there are still multiple future research challenges [1]. SR arises in several fields, such as remote sensing, surveillance, and an extensive set of consumer electronics applications [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Approach to Super-Resolution Through the Concept of Multicamera Imaging

Quevedo¹,

Marrero²,

Tobajas³

2016

Super-resolution consists of processing an image or a set of images in order to enhance the resolution of a video sequence or a single frame. There are several methods to apply super-resolution, from which fusion super-resolution techniques are considered to be the most adequate for real-time implementations. In fusion, super-resolution and highresolution images are constructed from several observed low-resolution images, thereby increasing the high-frequency components and removing the degradations caused by the recording process of low-resolution imaging acquisition devices. Moreover, the proposed imaging system considered in this work is based on capturing various frames from several sensors, which are attached to one another by a P × Q array. This framework is known as a multicamera system. This chapter summarizes the research conducted to apply fusion super-resolution techniques to select the most adequate frames and macroblocks together with a multicamera array. This approach optimizes the temporal and spatial correlations in the frames and reduces as a consequence the appearance of annoying artifacts, enhancing the quality of the processed high-resolution sequence and minimizing the execution time.