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Learning-Based Single Image Super-Resolution with Improved Edge Information

  • MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING
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Abstract

A new learning-based single image super-resolution technique that upscales the low resolution (LR) image in a single pass toits desired high resolution (HR) image is proposed here.Inthe upscaling procedure, a linearmapping function is learned from the external data set. Mapping function converts LR patch to its corresponding patch.In most of the patch-based learning technique, smoothness of the overlapped regions is performed with an average value of the overlapped regions. As a result, edge information that reflects in adjacent LR patches does not always transparently reflects in HR patches. So in our technique, we applied edge directed smoothness in adjacent patches. An edge exists along the direction, where the second-order derivative is lower. To reach this, we have selected non-overlapping patch, and after getting HR patch, we performed edge directed smoothness of adjacent patches. This results smoothness of adjacent patches with more detailedge information. Apart from this nonoverlapping patch selection reduces computational complexity, without compromising image quality. Experimental results show significant improvement in terms of subjective and objective quality than other popular learning or interpolation based method.Our method showsrobustness on noisy images also.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, 722146, Bankura, India

    G. Mandal

  2. Department of Computer Science and Engineering, Jadavpur University, 700032, Kolkata, India

    D. Bhattacharjee

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  1. G. Mandal
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  2. D. Bhattacharjee
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Correspondence to G. Mandal or D. Bhattacharjee.

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Additional information

Gitanjali Mandal. Completed B.Tech in Computer Science and Engineering from the University of Kalyani in 2004. She has completed her M.Tech in Information Technology from the University of Calcutta in 2008. She is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Bankura Unnayani Institute of Engineering. She has a teaching experience of 10 year. Her research interest includes image super-resolution, watermarking, steganography, etc.

Debotosh Bhattacharjee is working as a full professor in the Department of Computer Science and Engineering, Jadavpur University with fourteen years of post-PhD experience. His research interests pertain to the applications of machine learning techniques for Face Recognition, Gait Analysis, Hand Geometry Recognition, and Diagnostic Image Analysis. He has authored or coauthored more than 250 journals, conference publications, including several book chapters in the areas of biometrics and medical image processing. Two US patents have been granted on his works. Prof. Bhattacharjee has been granted sponsored projects by the Govt. of India funding agencies like Department of Biotechnology (DBT), Department of Electronics and Information Technology (DeitY), University Grants Commission (UGC) with a total amount of around INR 2 Crore. For postdoctoral research, Dr Bhattacharjee has visited different universities abroad like the University of Twenty, The Netherlands; Instituto Superior Técnico, Lisbon, Portugal; University of Bologna, Italy; ITMO National Research University, St. Petersburg, Russia; University of Ljubljana, Slovenia; Northumbria University, Newcastle Upon Tyne, UK and Heidelberg University, Germany. He is a life member of Indian Society for Technical Education (ISTE, New Delhi), Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI), and a senior member of IEEE (USA).

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Mandal, G., Bhattacharjee, D. Learning-Based Single Image Super-Resolution with Improved Edge Information. Pattern Recognit. Image Anal. 30, 391–400 (2020). https://doi.org/10.1134/S1054661820030189

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