Browsing by Author "CHEKHAR, Bakir Saber"

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    a DL model for image resolution enhancement and optimization for edge devices
    (université Ghardaia, 2024) DOUDOU, Yahia; CHEKHAR, Bakir Saber; Abdelkader, Bouhani Supervisor
    In recent years, deep learning-based single image super-resolution (SISR) has attracted considerable attention and achieved significant success on advanced GPUs. Most state- of-the-art methods require a large number of parameters, memory, and computational resources, often resulting in inferior inference times on mobile devices. In this thesis, we introduce a plain convolution network augmented with a nearest- neighbor convolution module and 8-bit quantization to achieve real-time SISR on NPUs. Furthermore, we evaluate the efficiency of our network architecture by comparing ex- periments on mobile devices to select the tensor operations to implement. The model comprises only 52 K parameters, achieves 4× upscaling in 0.065 s on a Snapdragon 865 CPU smartphone, and by comparing to other SR methods, we found that our model can achieve high fidelity super resolution results while using fewer inference times.
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    DL model for image resolution enhancement and optimization for edge devices
    (université Ghardaia, 2025) DOUDOU, Yahia; CHEKHAR, Bakir Saber; Bouhani, Abdelkader Supervisor
    In recent years, deep learning-based single image super-resolution (SISR) has attracted considerable attention and achieved significant success on advanced GPUs. Most state- of-the-art methods require a large number of parameters, memory, and computational resources, often resulting in inferior inference times on mobile devices. In this thesis, we introduce a plain convolution network augmented with a nearest- neighbor convolution module and 8-bit quantization to achieve real-time SISR on NPUs. Furthermore, we evaluate the efficiency of our network architecture by comparing ex- periments on mobile devices to select the tensor operations to implement. The model comprises only 52 K parameters, achieves 4× upscaling in 0.065 s on a Snapdragon 865 CPU smartphone, and by comparing to other SR methods, we found that our model can achieve high fidelity super resolution results while using fewer inference times.