Morphological Classification of Galaxies Using SpinalNet

Shaiakhmetov, Dim; Mekuria, Remudin Reshid; Isaev, Ruslan; Unsal, Fatma

doi:10.1109/icecco53203.2021.9663784

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…More recently, Shaiakhmetov et al proposed SpiralNet [4], a novel framework that effectively reduced computational costs while achieving an 82% accuracy on a 10-class task on Galaxy Zoo dataset. R. Dagli set a new state-of-the-art baseline with Astroformer [5], a transformer-convolutional hybrid model on Galaxy 10 DECals dataset that provides a 10-class classification task.…”

Section: Introductionmentioning

confidence: 99%

Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images

2023

ACE

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Images of galaxy objects are of great importance to the work of astronomers. Nowadays, the task of galaxy classification has been aided by Neural-Network-based classification models, who are powerful yet vulnerable to the attack of noisy images. In this research, RGB noises and bright spots simulating stars were generated, and a Convolutional-Neural-Network (CNN) based lightweight denoising image autoencoder was proposed. Firstly, a benchmark CNN classifier using DenseNet structure was trained on the Galaxy 10 DECals dataset, which consists of over 17, 000 RGB color galaxy images. Then, noisy images were generated by adding bright spots of different size and color simulating stars and applying gaussian RGB noises over the original images. The CNN autoencoder that consists of Convolutional layer in its encoder and Convolution Transpose layers in decoder was trained on the raw and noisy training data to learn effective galaxy image denoising. Finally, the effect of the autoencoder was evaluated by contrasting the performance of the CNN classifier over the noisy and denoised images. In contrast with being evaluated on the raw testing set, the CNN classifiers accuracy dropped by 0.41 when tested on the generated noisy testing images, indicating the effectiveness of the attack of image noises. While after denoising with the proposed autoencoder, the classifiers accuracy increased significantly by 0.37. Output denoised images also suggest that the autoencoder can effectively remove the applied bright spot and gaussian RGB noise, recreating the original shape of the galaxy.

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Section: Introductionmentioning

confidence: 99%

Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images

2023

ACE

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SpinalNet: Deep Neural Network With Gradual Input

Kabir

Abdar

Jalali

et al. 2023

IEEE Trans. Artif. Intell.

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Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism

Ryu

2023

Applied Sciences

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The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch. The deep analysis of the proposed framework shows that the model uses features and a mechanism that improves the monotonicity relationship between objective and subjective ratings. The intermediary goal was mapped to a quality score using a regression architecture. To extract various feature maps, a deep architecture with an adaptive receptive field was used. Analyses of this biggest NR-IQA benchmark datasets demonstrate that the suggested technique outperforms current state-of-the-art NR-IQA measures.

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Morphological Classification of Galaxies Using SpinalNet

Cited by 3 publications

References 16 publications

Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images

Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images

SpinalNet: Deep Neural Network With Gradual Input

Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism

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