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- 저자 : Lehri, Liaquat Ali (검색결과 2 건)
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LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.
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LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.
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