Manuscript Topics

Due to arise of big data and developments of internet technologies, images have been widely applied in image diagnosis, face recognition, intelligent transportation and weather forecast. However, collected images by digital camera devices often suffer from some drawbacks of photographic distances, camera shakes, moving objects and hardware effects, which make obtained images blurring. That seriously affects high-level vision tasks, i.e., image recognition. Besides, image recognition task with no referenced categories is very difficult to obtain a robust classifier for complex scenes. How to deal with corrupted image recognition is very important. Due to strong learning abilities, deep learning techniques are developed in image applications. Thus, deep learning techniques for corrupted image recognition is very valuable for academia and industry. Inspired by that, we host a SI to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results of deep learning techniques for corrupted image recognition. Topics of interest include, but are not limited to, the following:

• corrupted image denoising by machine and deep learning  
• corrupted image super-resolution by machine and deep learning  
• AI-empowered corrupted image deblurring
• low-light image enhancement by machine and deep learning
• AI-oriented image classification
• object detection of corrupted images by machine and deep learning
• AI-based medical image processing and recognition  
• DL/ML of image recognition and processing

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