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No-reference (NR) image sharpness assessment is an important issue for image quality assessment and algorithm performance evaluation. Many objective NR sharpness assessment metrics have been proposed which are often intended to be strongly associated with the human visual system (HVS). However, recent studies show that common sharpness assessment indicators may misjudge the degree of blurring for images with shallow depth of field that are often used to highlight the main subject in the view.

Quarter sampling is a novel sensor design that allows for an acquisition of higher resolution images without increasing the number of pixels. When being used for video data, one out of four pixels is measured in each frame. Effectively, this leads to a non-regular spatio-temporal sub-sampling. Compared to purely spatial or temporal sub-sampling, this allows for an increased reconstruction quality, as aliasing artifacts can be reduced. For the fast reconstruction of such sensor data with a fixed mask, recursive variant of frequency selective reconstruction (FSR) was proposed.

Warp-based methods for image animation estimate a warp
field what do a rearrangement on the pixels of the input image to roughly align with the target image. Current methods
predict accurate warp field by using manually annotated data.
In this paper, we propose a simple method (MAT-net) to predict more precise warp field in self-supervised way. MAT-net
decomposes complex spatial object movement between two

We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination.

Benefiting from learning the residual between low resolution (LR) image and high resolution (HR) image, image super-resolution (SR) networks demonstrate superior reconstruction performance in recent studies. However, for the images with rich texture information, the residuals are complex and difficult for networks to learn. To address this problem, we propose a recurrent residual refinement network (RRRN) to gradually refine the residual with a recurrent structure.

We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination. First, a simple yet effective color correction approach is used to remove color casts and recover naturalness. Then, a variational retinex model for enhancing the color-corrected underwater image is established by imposing multiorder gradient priors of reflectance and illumination.