Image Enhancement and Restoration Based on Deep Learning Technology

Dear Colleagues,

An important goal of digital imaging is producing scenes with good contrast, vivid color, and rich details. However, some issues, such as low intensity, defocusing, low resolution, object occlusions, and inclement weather, degrade the quality of images. A downgraded low-resolution image does not provide enough information in various computer vision applications. A nonlinear mapping from the low-resolution to super-resolution to reconstruct a clear and high resolution image is important and a fundamental task. Unfavorable weather causes significant problems to image quality as the rain or snow often occludes or blurs the scene information. Image enhancement and restoration are critical processes in various computer vision applications, such as security, surveillance imaging, medical imaging, image recognition, computational photography, and remote sensing. Due to the diversity of imaging sensors and mechanisms, multiple modality images may need to be fused together to enhance image quality. Recently, deep learning has been widely used in image enhancement and restoration and has achieved great success due to its superior ability to extract features. In addition, the deep convolutional neural network is also used for single image enhancement. In this Special Issue, we cover wide approaches for robust image enhancement and restoration based on deep learning technology.

Dr. Hyo Jong Lee
Prof. Dr. Yong Yang
Guest Editors

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