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Transpose convolution based model for super-resolution image reconstruction

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Abstract

Single image resolution is a noticeably challenging issue that targets to acquire a high-resolution output out of one of its low-resolution variants. Many existing approaches for single-image resolutions are based on the direct solving details by using pre-defined up sampling operators. Therefore, it is challenging for the reconstruction process when the image has a larger upsampling factor. Recently, convolution neural networks (CNNs) made easy progress on super-resolution (SR) image with good results. However, the majority of methods are based on pre-defined up sampling, which uses the bicubic interpolation technique for upscaling the low-resolution (LR) image and employs feature maps to reconstruct the final high-resolution (HR) image. This leads to visual artifacts in reconstructed images and can be difficult to train such a model with a larger network. Therefore, we remove the proposed transposed convolution layer method with a novel architecture and avoid the usage of pre-defined up sampling operators. We purpose an efficient method for the usage of transposed convolution with a new architecture design and use a recurrent residual block for mapping extraction in a step-by-step manner. Finally, we generate the desired super-resolution image with low complexity and fewer parameters. Experiments and state-of-art results show better performance than existing models.

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Authors and Affiliations

  1. National Mobile Communication Research Laboratory, Southeast University, Nanjing, China

    Faisal Sahito & Pan Zhiwen

  2. College of Electronic and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing, China

    Fahad Sahito

  3. Department of Electrical Engineering, Sukkur IBA University, Sukkur, 65200, Pakistan

    Junaid Ahmed

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  1. Faisal Sahito
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  2. Pan Zhiwen
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  3. Fahad Sahito
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Correspondence to Faisal Sahito.

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Sahito, F., Zhiwen, P., Sahito, F. et al. Transpose convolution based model for super-resolution image reconstruction. Appl Intell 53, 10574–10584 (2023). https://doi.org/10.1007/s10489-022-03745-4

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