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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Velmurugan, Athiyoor Kannan
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Enhancing single image dehazing with self-supervised convolutional neural network and dark channel prior integration Hari, Unnikrishnan; Bajulunisha, Alla Bukshu; Pandey, Pramod; Rexi, Joseph Arul Michiline; Sujatha, Velusamy; Raj, Thankappan Saju; Velmurugan, Athiyoor Kannan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp520-528

Abstract

The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-supervised convolutional neural network (CNN). This approach incorporates both the input hazy image and the dark channel prior. By leveraging an encoder, the combined information of the dark channel prior and haze image is encoded into a condensed latent representation. Subsequently, a decoder is employed to reconstruct the clean image using these latent features. Our experimental results demonstrate that our proposed algorithm significantly enhances image quality, as indicated by improved peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. We perform both quantitative and qualitative comparisons with recently published techniques, showcasing the efficacy of our approach.
SIGAN: a generative adversarial network architecture for sketch to photo synthesis Latha, Buddannagari; Velmurugan, Athiyoor Kannan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3118-3126

Abstract

Of late, with the rise of artificial intelligence (AI) and deep learning (DL) models, image translation has become a very important phenomena which could produce realistic photographic results. Synthesizing new images is widely used in different applications including the ones used by investigation agencies. Image generation from hand-drawn sketch to realistic photos and vice versa is required in different computer vision applications. Generative adversarial network (GAN) architecture is extensively employed for generating images. However, there is need for investigating further on improvising GAN architecture and the underlying loss functions towards leveraging performance. In this paper, we put forth a GAN architecture known as sketch-image GAN (SIGAN) for synthesizing realistic photos from hand-drawn sketches. Both generator (G) and discriminator (D) components are designed based on DL models following a non-cooperative game theory towards improving image generation performance. SIGAN exploits improvised image representation and learning of data distribution. The algorithm we have proposed is known as learning-based sketch-image generation (LbSIG). This algorithm exploits SIGAN architecture for efficiently generating realistic photo from given hand-drawn sketch. SIGAN is assessed using a benchmark dataset called CUHK face sketch database (CUFS). From the empirical study, it is observed that the proposed SIGAN architecture with underlying deep learning models could outperform existing GAN models in terms of Fréchet inception distance (FID) with 38.2346%.
Co-Authors Bajulunisha, Alla Bukshu Begum, Akbar Sumaiya Hari, Unnikrishnan Kishore, Somala Rama Komathi, Arunachalam Latha, Buddannagari Muthukumaran, Dhakshnamoorthy Pandey, Pramod Pavithra, Maddipetlolu Rajendran Raj, Thankappan Saju Rexi, Joseph Arul Michiline Selvaraj, Yoganand Sujatha, Velusamy