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Face super-resolution has been studied for decades, and many approaches have been proposed to upsample low-resolution face images using information mined from paired low-resolution (LR) images and high-resolution (HR) images. However, most of this kind of works only simply sharpen the blurry edges in the upsampled face images and typically no photo-realistic face is reconstructed in the final result. In this paper, we present a GAN-based algorithm for face super-resolution which properly synthesizes photo-realistic super-recovered face.

In medical practice, the X-ray Computed tomography-based scans expose a high radiation dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose CT scan can reduce radiation exposure to the patient. But the reduced radiation dose degrades image quality for human perception, and adversely affects the radiologist’s diagnosis and prognosis. In this paper, we introduce a GAN based auto-encoder network to de-noise the CT images.

State-of-the-art face recognition methods have achieved ex- cellent performance on the clean datasets. However, in real- world applications, the captured face images are usually contaminated with noise, which significantly decreases the performance of these face recognition methods. In this pa- per, we propose a cascaded noise-robust deep convolutional neural network (CNR-CNN) method, consisting of two sub- networks, i.e., a denoising sub-network and a face recognition sub-network, for face recognition under noise.

Reliably predicting where people look in images and videos remains challenging and requires substantial eye-tracking data to be collected and analysed for various applications. In this paper, we present an eye-tracking study where twenty-eight participants viewed forty still scenes of video advertising. First, we analyse human attentional behaviour based on gaze data. Then, we evaluate to what extent a machine – saliency model – can predict human behaviour. Experimental results show that there is a significant gap between human and machine in visual saliency.

Our previous study has shown that image distortions cause saliency distraction, and that visual saliency of a distorted image differs from that of its distortion-free reference. Being able to measure such distortion-induced saliency variation (DSV) significantly benefits algorithms for automated image quality assessment. Methods of quantifying DSV, however, remain unexplored due to the lack of a benchmark. In this paper, we build a benchmark for the measurement of DSV through a subjective study.

Lossy image compression aims to encode images with a low bit-rate representation while preserving a pleasant visual quality of decompressed images. By utilizing the manually designed features, the traditional compression may not be suitable for diverse image content and may cause visible artifacts under the low bit rate constraint. Recently, deep learning based methods, which can extract the compact representation of an image in an auto-encoder way, were proposed for image compression.

Non-local sparsity has been widely concerned in image compressive
sensing. Considering the difference of distribution
characteristic of among group-based sparse coefficients of
image, a new method for image compressive sensing reconstruction
(ICSR) is proposed based on the z-scores standardized
group sparse representation (ZSGSR). Here, the
similar patch groups of the image are firstly extracted and
decomposed by adaptive PCA dictionary, then the resulting
coefficients are normalized using z-score standardization in