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Compressed Image Super Resolution using Convolutional Neural Network 38

Compressed Image Super Resolution using Convolutional Neural...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Tun, Ei Ei Konkitkriengkrai, Aktanin Ruangsang, Watchara Aramvith, Supavadee Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9798350302196

Image compression is a topic of significant interest as it reduces file sizes in stored data. In this paper, we propose a model that achieves multiple levels of compression, thereby minimizing the storage space required for images, which typically consume substantial amounts of data due to their size and resolution. We combine an image downscaling and upscaling model with an image compression model. By leveraging convolutional techniques to identify image features, we can effectively reduce the size of the image through downscaling and subsequently upscaling it. Additionally, we employ entropy image compression and arithmetic encoding to compress and reconstruct the image while preserving its lossless data. Through experimentation with the Kodak dataset, we observed that our proposed model achieved a compression rate of 96.92%, significantly reducing the data needed for file storage. Moreover, our reconstructed images attained a standardized measure with a signal-to-noise ratio of 33.10 dB and a structural similarity of 0.9219. Notably, the perceptual quality of the images, including intricate details, remained intact to the human eye. © 2023 IEEE.

关键词： Image reconstruction

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Enhancing Visual Fidelity in Unpaired Night-to-Day Image Translation through a Perceptual Quality Focused Training Objective 7

Enhancing Visual Fidelity in Unpaired Night-to-Day Image Tra...

引用

7th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Lakmal, H.K.I.S. Dissanayake, Maheshi B. Aramvith, Supavadee University of Peradeniya Faculty of Engineering Dept. of Electrical and Electronic Engineering Peradeniya Sri Lanka Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350319262

Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily be adept to night-to-day image translation tasks. However, it generates unnatural and unrealistic outcomes in these cases. This study is focused on addressing this with a novel training strategy for the Cycle-GAN, employing a tailored training objective that incorporates perceptual quality optimization. This training objective aims to boost the naturalness and perceptual quality of the generated images by preserving high-level image features. The optimization process involves minimizing Euclidean distances between synthesized and target image feature maps, which are derived from the pre-trained VGG19 network. Experimental findings attest to the effectiveness of this method, revealing noteworthy improvements of 8% in Inception Score, 2% in NIQE Score, and 13% in BRISQUE Score. © 2023 IEEE.

关键词： Image enhancement

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Combined Channel and Spatial Attention-Based Stereo Endoscopic Image Super-Resolution 38

Combined Channel and Spatial Attention-Based Stereo Endoscop...

引用

38th IEEE Region 10 Conference, TENCON 2023

作者： Hayat, Mansoor Armvith, Supavadee Achakulvisut, Titipat Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand Mahidol University Department of Biomedical Engineering Bangkok Thailand

ISBN: (纸本)9798350302196

Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective. © 2023 IEEE.

关键词： Geometrical optics

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Estimation of Eucalyptus DBH from UAV-LiDAR data Utilizing Advanced Point Cloud processing Techniques 9

Estimation of Eucalyptus DBH from UAV-LiDAR Data Utilizing A...

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9th International Conference on Business and Industrial research, ICBIR 2024

作者： Dangrungroj, Peerapong Udompitaksook, Monton Intarasuk, Natapong Pluempitiwiriyawej, Charnchai Silawan, Teerapol Laboratory Department of Electrical Engineering Bangkok Thailand Sky Visual Imaging Venture Co. Ltd Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350383010

The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, especially in inaccessible forests. Existing methods, including manual measurement or remote sensing, struggle with precision in low-intensity point cloud areas. Our technique solves these limitations by analyzing the entire tree stem by the process, including convex hull construction and advanced noise reduction using real-world datasets. It has reduced the Root Mean Squared Error (RMSE) to 0.020, or 92.25% improvement, and the bias has decreased to -0.008. These enhancements highlight the accuracy and reliability of our DBH estimation technique. © 2024 IEEE.

关键词： Noise abatement

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2D T1-Weighted CE MRI Images Classification with Hybrid Channel Attention Block enhanced VGG-16 and MobileNet

2D T1-Weighted CE MRI Images Classification with Hybrid Chan...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Mohammad Jawad Fareed Charnchai Pluempitiwiriyawej Ghulam Murtaza Department of Electircal Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight methodologies, although explored extensively, have fallen short of achieving the accuracy benchmarks attained by the model. The approach underwent rigorous testing on a challenging 2D T1-weighted CE-MRI dataset housing three distinct types of brain tumors: Meningioma, Glioma, and Pituitary. Recognizing the susceptibility of models to overfitting during training, introduced was a normalized combined channel and spatial attention mechanism, strategically incorporated to serve as an effective regularizer. Comparative analysis against state-of-the-art methodologies on this dataset highlights a notable performance boost of 2.12 percentage points achieved by integrating this attention mechanism into a base network. Furthermore, the fusion of the model with the pre-trained VGG16 network and GoogleNet into an ensemble demonstrates superior accuracy; however, this amalgamation comes at the expense of execution speed.

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International Conference on Applied Intelligence and Sustain...

引用

International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023 2023年

作者： Aramvith, Supavadee Electrical Engineering Chulalongkorn University India Internationalization Head Multimedia Data Analytics and Processing Research Unit Thailand

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Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16

Efficient Deep Attentive Pixels Network in Face Super-Resolu...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Hein Htet Aung Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models.

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Enhanced Cross-Modality MRI Segmentation Using Dilated Convolutions and Multi-Scale Gradient Map 28

Enhanced Cross-Modality MRI Segmentation Using Dilated Convo...

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28th International Computer Science and Engineering Conference, ICSEC 2024

作者： Murtaza, Ghulam Pluempitiwiriyawej, Charnchai Wangsiripitak, Somkiat Fareed, Mohammad Jawad Khalid, Mudassar Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand King Mongkut's Insittute of Technology Machine Intelligence and Vision Laboratory School of Information Technology Ladkrabang Thailand

ISBN: (纸本)9798350366860

In recent years, convolutional neural networks have significantly advanced image segmentation, particularly for brain images, where important edge features are automatically found. However, accurate segmentation of tumors in a brain remains a challenge across different magnetic resonance modalities, like T1, T2, T1ce, and FLAIR. Using a simple gradient map as an input to the neural networks is not effective due to variations in cross-modality image characteristics. To address this issue, we introduced multi-scale gradient maps that incorporate Holistically Nested Edge Detection (HED) and dilated convolutions into the UNet model. The HED model captures detailed gradient information, enhancing structural feature identification across modalities, while dilated convolutions expand the UNet receptive field for better contextual understanding without increasing parameters. Our method was trained and evaluated on the BraTS2018 dataset. The experimental results demonstrate significant improvements in segmentation accuracy and robustness. Specifically, our method achieved a Dice Similarity Coefficient (DSC) of 0.6902 for T2 to T1ce, 0.6858 for T2 to T1, 0.4329 for FLAIR to T1, and 0.6004 for FLAIR to T1ce, outperforming previous state-of-The-Art methods. This demonstrates the effectiveness of our approach in enhancing segmentation performance across different MR image modalities. © 2024 IEEE.

关键词： Image segmentation

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Non-Local Technique on Deep Attentive Face Super-Resolution Network

Non-Local Technique on Deep Attentive Face Super-Resolution ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Amir Hajian Hein Htet Aung Watchara Ruangsang Sovann Chen Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

Recent Face Super-resolution (FSR) based on iterative collaboration between facial image recovery network and landmark estimation has succeeded in super-resolving facial images. However, the existing noise in coarse features at the low-level feature extraction leads to inaccurate facial priors such as landmarks and component maps, consequently degrading the super-resolved face image on a large scale. This paper proposes, a Non-local technique for deep attentive face super-resolution network (NLDA). A Non-local module has been designed before the residual channel attention block (RCAB) to eliminate noise degradation on coarse features effectively. The proposed model optimizes feature extraction and improves facial landmark fusion to yield higher-quality super-resolved images. This approach facilitates more accurate landmark estimation and boosts the performance of our model on a large scale and various face poses. Quantitative and qualitative experiments over CelebA and Helen face image datasets show that the proposed method outperforms other state-of-the-art FSR methods in recovering high-quality face images in various face poses and at a large scale.

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An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

An Advanced Features Extraction Module for Remote Sensing Im...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Naveed Sultan Amir Hajian Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In recent years, convolutional neural networks (CNNs) have achieved remarkable advancement in the field of remote sensing image super-resolution due to the complexity and variability of textures and structures in remote sensing images (RSIs), which often repeat in the same images but differ across others. Current deep learning-based super-resolution models focus less on high-frequency features, which leads to suboptimal performance in capturing contours, textures, and spatial information. State-of-the-art CNN-based methods now focus on the feature extraction of RSIs using attention mechanisms. However, these methods are still incapable of effectively identifying and utilizing key content attention signals in RSIs. To solve this problem, we proposed an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) for effectively extracting the features by using the channel and spatial attention incorporated with the standard vision transformer (ViT). The proposed method trained over the UCMerced dataset on scales 2, 3, and 4. The experimental results show that our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones, which enhances the quality of super-resolved images. Our model achieved superior performance compared to various existing models.

关键词： Computational modeling Superresolution Feature extraction Transformers Decoding Telecommunications Spatial resolution

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