An overview of computational photography

Abstract

Computational photography is an emerging multidisciplinary field. Over the last two decades, it has integrated studies across computer vision, computer graphics, signal processing, applied optics and related disciplines. Researchers are exploring new ways to break through the limitations of traditional digital imaging for the benefit of photographers, vision and graphics researchers, and image processing programmers. Thanks to much effort in various associated fields, the large variety of issues related to these new methods of photography are described and discussed extensively in this paper. To give the reader the full picture of the voluminous literature related to computational photography, this paper briefly reviews the wide range of topics in this new field, covering a number of different aspects, including: (i) the various elements of computational imaging systems and new sampling and reconstruction mechanisms; (ii) the different image properties which benefit from computational photography, e.g. depth of field, dynamic range; and (iii) the sampling subspaces of visual scenes in the real world. Based on this systematic review of the previous and ongoing work in this field, we also discuss some open issues and potential new directions in computational photography. This paper aims to help the reader get to know this new field, including its history, ultimate goals, hot topics, research methodologies, and future directions, and thus build a foundation for further research and related developments.

Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

“…To generate a high-resolution image with photographgrade realistic details and inspired by the literature [22,23], we propose a simple and effective two-layer GAN, in The images in the first row are original images, those in the second row are the reconstruction results using the SRCNN method, those in the third row are the reconstruction results using the ESPCN method, those in the fourth row are the reconstruction results using the SRGAN method, those in the fifth row are the reconstruction results using the ESRGAN method, those in the sixth row are the reconstruction results using the ESRGAN+ method, and those in the last row are the reconstruction results using our proposed method. 4 International Journal of Digital Multimedia Broadcasting which the image generation process is divided into two stages (Figure 2). The images in the first row are original images, those in the second row are the reconstruction results using the SRCNN method, those in the third row are the reconstruction results using the ESPCN method, those in the fourth row are the reconstruction results using the SRGAN method, those in the fifth row are the reconstruction results using the ESRGAN method, those in the sixth row are the reconstruction results using the ESRGAN+ method, and those in the last row are the reconstruction results using our proposed method.…”

“…Although it is possible to improve the hardware performance of the infrared imaging system by improving the manufacturing process of the infrared detector, it requires tremendous human and financial resources and is difficult to achieve in the short term. Therefore, digital signal processing is an economical and effective way to improve the quality of infrared images [2][3][4]. Superresolution reconstruction refers to the reconstruction of a high-resolution image or a sequence on single-frame or multiframe low-resolution images and includes three main types, i.e., interpolation-based methods, reconstruction-based methods, and instance-based learning methods.…”

Computational Photography, 3-D Modeling, and Online Publication of Basketry for Cache Cave, California

“…Additionally, technological developments have enhanced accessibility to artifacts via advanced computational photography tools, which have constantly evolved over the past four decades as computational photography has become a multidisciplinary field of research of particular interest to researchers, artists, photographers, and engineers. Computational photography lies at the intersection of computer vision, image processing, and computer graphics and incorporates sequence of images and a broad variety of techniques (Durand and Szeliski 2007; Suo et al 2012). In the field of archaeological research, it is useful for extracting features for research purposes (Miles 2018).…”

Accurate Pixel-to-Pixel Alignment Method With Six-Axis Adjustment for Computational Photography

“…Computational photography has been a new research focus over the last two decades, in that flexible imaging is achieved by modulating the imaging ray with the spatial light modulator (SLM) [1]- [4]. Most of computational imaging applications, such as digital micromirror device (DMD) camera profile measurement, dual-coded hyperspectral imaging, temporal pixel multiplex imaging, high dynamic range imaging and intra-pixel optical feature detection, involve pixel-to-pixel correspondence adjustment on the object-image conjugate architecture composed of the sensor and the SLM, i.e., accurate pixel-to-pixel alignment is one of the key technologies in computational photography [5]- [9].…”