Deep Learning Applications in Image Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 January 2026
Special Issue Editors
Interests: image processing; video coding; computer vision
Interests: computational image analysis; machine learning; interdisciplinary physics; tumor detection; medical image analysis
Special Issue Information
Dear Colleagues,
In the burgeoning era of artificial intelligence, deep learning has emerged as a pivotal force in image analysis, revolutionizing domains such as image denoising, super-resolution, and encoding. For image denoising, deep learning algorithms, particularly convolutional neural networks (CNNs), excel at automatically learning the mapping from noisy to clean images using extensive image pairs. By extracting multi-scale features, these algorithms suppress diverse noise types while safeguarding fine details, outperforming their traditional counterparts significantly.
In image super-resolution, deep neural networks, including super-resolution convolutional neural networks (SRCNNs) and their advanced variants, adeptly reconstruct high-resolution images from low-resolution inputs. These models effectively capture intricate textures and structures, yielding high-fidelity super-resolved outputs. In video super-resolution, leveraging the temporal coherence of video frames, recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) are often integrated with CNNs. This fusion capitalizes on adjacent frame information, enhancing the super-resolution effect and rendering videos clearer and smoother.
Regarding image and video encoding, deep learning demonstrates remarkable potential. Deep learning-based encoders optimize the encoding process by learning the statistical properties of images and videos. They dynamically adjust encoding parameters according to content complexity, striking an optimal balance between compression ratio and visual quality. Compared with conventional encoding standards, deep learning-driven methods achieve higher compression efficiency without compromising visual fidelity. As these techniques continue to evolve, they promise expansive applications in computer vision, multimedia, and beyond, driving sustained innovation across related industries.
Dr. Chao Yao
Dr. Nektarios A. Valous
Guest Editors
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Keywords
- artificial intelligence
- deep learning
- image analysis
- convolutional neural networks (CNNs)
- video encoding
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