Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

Muhammad, Wazir; Bhutto, Zuhaibuddin; Ansari, Arsalan; Memon, Mudasar Latif; Kumar, Ramesh; Hussain, Ayaz; Shah, Syed Muhammad Mukarram; Thaheem, Imdadullah; Ali, Shamshad

doi:10.3390/electronics10161979

Cited by 15 publications

(10 citation statements)

References 80 publications

(136 reference statements)

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“…In the field of image SR, efforts to reduce the computational complexity and save storage space have led to the exploration of lightweight SR networks [35][36][37]. Guo et al [38] proposed a Lightweight Multi-Dimension Feature Fusion Network (LMDFFN) for optical image SR, which maintains high reconstruction quality with fewer parameters and reduced computational complexity.…”

Section: Lightweight Technologymentioning

confidence: 99%

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Jiang,

Zhao,

Wang

et al. 2024

Remote Sensing

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Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.

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Section: Lightweight Technologymentioning

confidence: 99%

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Jiang,

Zhao,

Wang

et al. 2024

Remote Sensing

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“…Inception-V1 is another name for GoogLeNet, which won the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) competition. The GoogLeNet architecture's primary goal was to achieve excellent accuracy at a low cost of computing [66][67][68][69]. Inception block is a novel concept from CNN that combines split, transform, and merge techniques with multi-scale convolutional transformations.…”

Section: Inception (Googlenet) Blockmentioning

confidence: 99%

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Muhammad¹,

Bhutto²,

Masroor³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot receive complete information and work as a dead layer. In this paper, we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS. In our proposed approach, a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters. Furthermore, a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image. Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

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“…COVID-19 is usually diagnosed because of pneumonia-like symptoms, which can be detected through genetic and imaging studies. Deep learningbased convolutional neural networks (CNNs) has significant applications in various field such as image super-resolution, satellite imaging, security surveillance, and medical image classification tasks (Tajbakhsh et al, 2016;Peled and Yeshurun, 2001;Muhammad et al, 2020;2021a;2021b;Muhammad and Aramvith, 2019;Shi et al, 2013). Promising results of deep learning in the field of medical diagnosis have urged scientists to use it for COVID-19 detection as well.…”

Section: Related Workmentioning

confidence: 99%

Deep transfer learning CNN based approach for COVID-19 detection

al.¹

2022

Int. j. adv. appl. sci.

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Recently, the novel coronavirus (Covid-19) and its different variants have spread rapidly across the world. Early-stage detection of COVID-19 is a challenging task due to the limited availability of Covid testing kits to the public. Conventionally, reverse transcription-polymerase chain reaction (RT-PCR) is the reliable test for the detection of COVID-19 which is time-consuming and costly. The aim of this work is to identify the COVID-19 symptoms with the help of a deep learning algorithm using chest X-Ray images. In order to improve the quality of chest X-Ray images, authors have further modified the pre-trained model with some extra CNN layers, such as the first layer is the average pooling layer and the other two are dense layers followed by ReLU with softmax activation function. The experimental results have been carried out on publicly available chest X-Ray images of COVID-19 to mark COVID-19 patients as positive and negative datasets. For evaluation purpose, we have used benchmark of pre-trained models such as VGG-16 (Visual Geometry Group), VGG19, Xception, ResNet152, ResNet152v2, ResNet101, ResNet101v2, DenseNet201, DenseNet169 and DenseNet121. On the benchmark datasets, viz. COVID-19 X-Ray images, an average improvement in terms of training/validation accuracy, precision, recall, and F1-scores scores were 95%, 94%, 99/88%, 99/88%, and 93/92% respectively. The results provide sufficient evidence that deep learning can be used efficiently for the detection of COVID-19 symptoms.

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Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

Cited by 15 publications

References 80 publications

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Deep transfer learning CNN based approach for COVID-19 detection

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