5 results for “Autoencoders”

Set a search alert

Research ArticleOpen accessEvery word in the titlePedestrian Detection in RGB-D Data Using Deep AutoencodersPavel Aleksandrovich Kazantsev, Pavel Vyacheslavovich SkribtsovRecent popularity of RGB-D sensors mostly comes from the fact that RGB-images and depth maps supplement each other in machine vision tasks, such as object detection and recognition. This article addresses a problem of RGB and depth data fusion for pedestrian detection. We propose pedestrian detection algorithm that involves fusion of outputs of 2D- and 3D-detectors based on deep autoencoders. … …American Journal of Applied Sciences, Vol. 12, Issue 11 · 17 Nov 2015 · 6,812 accessesView PDF Cite

Review ArticleOpen accessEvery word in the titleDenoising of Underwater Images with Regulated AutoencodersShreya Priyadarshini Roy, N Maheswari, M Sivagami, Angelin Beulah SIn the domain of underwater image processing, the images obtained in underwater lighting conditions are of very poor in quality and contain distortions due to the scattering of lights and color absorption of images in water. This makes it difficult to analyze these images for scientific, commercial and military purposes. Therefore, there is a need for efficient techniques that can … …Journal of Computer Science, Vol. 20, Issue 6 · 4 Apr 2024 · 5,571 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleRegularization Method for Solving Denoising and Inpainting Task Using Stacked Sparse Denoising AutoencodersPavel Vyacheslavovich Skribtsov, Sergey Olegovich SurikovThis article offers a regularization method for training stacked sparse denoising autoencoders aimed at designing model description of objects used for image denoising and inpainting. The offered regularization method allows increasing the generalizing ability of model description, which results in greater stability of denoising methods using it with regard to variation of the noise type. This makes the offered method … …American Journal of Applied Sciences, Vol. 13, Issue 1 · 13 Jan 2016 · 7,609 accessesView PDF Cite

Review ArticleOpen accessIn the keywordsDeepfake Technology: A Comprehensive Review of Trends, Applications, Ethical Concerns, and ChallengesBattula Thirumaleshwari Devi, Rajkumar RajasekaranThe digital age has fundamentally transformed how information is created and disseminated, raising critical concerns about the authenticity and trustworthiness of online content. Recent advances in Artificial Intelligence (AI), particularly deep learning, have given rise to deepfakes: highly realistic synthetic media generated by manipulating or replacing faces, voices, and actions in videos. While deepfake technology offers innovative applications across various … …Journal of Computer Science, Vol. 22, Issue 1 · 17 Feb 2026 · 4,861 accessesView PDF Cite

Review ArticleOpen accessIn the abstractUnleashing the Power of Image Denoising: A Comprehensive Review of Classical to Deep Learning MethodsArchana Saini, Ayush DograImage denoising is a vital step in many image processing and computer vision professions that aims to improve picture quality by decreasing noise while maintaining important image information. In this article, a detailed overview is presented of traditional and deep learning based picture denoising approaches. Classical methods, such as linear filtering, transform domain techniques, and patch-based approaches like Non-Local Means … …Journal of Computer Science, Vol. 21, Issue 8 · 8 Oct 2025 · 3,107 accessesView PDF Cite