Image Processing

Realistic benchmark datasets are crucial for providing a consistent measurement baseline for comparing different BlindSR methods and testing their generalization ability in real-world scenarios. However, the evaluation of BlindSR methods is still limited by the lack of a common dataset that conforms to realistic degradation scenarios. We construct a benchmark dataset that follows real scenarios, which reflects the real-world BlindSR problem more accurately than existing synthetic datasets.

The morphological characteristics of skeletal muscles, such as fascicle orientation, fascicle length, and muscle thickness, contain valuable mechanical information that aids in understanding muscle contractility and excitation due to commands from the central nervous system. Ultrasound (US) imaging, a non-invasive measurement technique, has been employed in clinical research to provide visualized images that capture morphological characteristics. However, accurately and efficiently detecting the fascicle in US images is challenging.

In industrial microscopic detection, learning-based autofocus methods have empowered operators to acquire high-quality images quickly. Learning-based methods consist of two parts: network model and prior dataset. The network model, which approximates the relationship between input and output, exists fitting error. The prior dataset is made by sharpness metric and used for model training, while the limitations of the metric itself will affect the accuracy of the dataset.

It is a challenging work to solve the geometric attack in the field of digital watermarking. In order to solve the synchronization between the host image and the watermark, image normalization is introduced. Firstly, the geometrically invariant space of image is constructed by using image normalization, and a region of interest (ROI) is obtained from the normalized image by utilizing the invariant centroid theory. Then, the contourlet transform is performed on the ROI. Low-pass sub-band coefficients are divided into non-overlapping blocks.