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Toward image super-resolution based on local regression and nonlocal means

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Abstract

Super-resolution algorithms have many weaknesses because they have low resolution and quality. These weaknesses are due to limitations and high cost of hardware implementation. Therefore, software methods are used due to its high accuracy and low cost. Super-resolution is used in a variety of applications, including satellite imagery, medicine, CCTV cameras, etc. Super-resolution is divided into two main categories: super-resolution using an input image and super-resolution using multiple input images. Considering the great use of the subject, this paper provides a method for increasing the resolution using an input image. In the proposed method, we improved the image super-resolution based on local regression and nonlocal means. The proposed method consists of two stages: a learning phase and a reconstruction phase. After the initial implementation, we compared the results with a quick and accurate method and found the difference. Then, using the Genetic algorithm, these differences were applied to the initial image. Results showed that our proposed method performed 2.4% and 2.2% better than the RAISR and LANR-NLM methods, Respectively.

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Author notes

  1. Mohammad Mohammadi

    Present address: Department of Media Engineering, IRIB University, Tehran, Iran

Authors and Affiliations

  1. Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Azam Bastanfard

  2. Department of Computer Science and Engineering, University of Quebec in Qutaouais, Gatineau, Canada

    Dariush Amirkhani

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  1. Azam Bastanfard
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  2. Dariush Amirkhani
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  3. Mohammad Mohammadi
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Correspondence to Azam Bastanfard.

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Bastanfard, A., Amirkhani, D. & Mohammadi, M. Toward image super-resolution based on local regression and nonlocal means. Multimed Tools Appl 81, 23473–23492 (2022). https://doi.org/10.1007/s11042-022-12584-x

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