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Residual in Residual Cascade Network for Single-Image Super Resolution

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Advances in Information Communication Technology and Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 392))

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Abstract

Digital images are of great significance in today’s scenario, where almost every single domain relies on them from agriculture to businesses to the military for their specific purposes. To convey the context, a more precise image gives more idealization and personalization to the aspect one wants to build. The precise images connote high clarity and distinct characteristics which is represented by a parameter known as resolution of image. This paper focuses on upscaling the resolution of image using a fast, lightweight artificial neural network (ANN)—RRCasN. The proposed model surpasses the traditional methods and competes with modern ANN-based architectures in terms of size of the model and quality of the high-resolution image produced. This paper also introduces a novel approach for normalization in place of batch normalization.

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Authors and Affiliations

  1. Division of Information Technology, Netaji Subhas University of Technology, New Delhi, Delhi, 110078, India

    Anirudh Aggarwal, Mohit Bansal, Tanishq Verma & Apoorvi Sood

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  1. Anirudh Aggarwal
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  2. Mohit Bansal
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  3. Tanishq Verma
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  4. Apoorvi Sood
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Correspondence to Apoorvi Sood .

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Editors and Affiliations

  1. Government Engineering College Bikaner, Bikaner, Rajasthan, India

    Vishal Goar

  2. Government Engineering College Bikaner, Bikaner, Rajasthan, India

    Manoj Kuri

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. University of the Ryukyus, Nishihara, Japan

    Tomonobu Senjyu

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Aggarwal, A., Bansal, M., Verma, T., Sood, A. (2022). Residual in Residual Cascade Network for Single-Image Super Resolution. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 392. Springer, Singapore. https://doi.org/10.1007/978-981-19-0619-0_30

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  • DOI: https://doi.org/10.1007/978-981-19-0619-0_30

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