Identifying low-resolution (LR) face images in the wild is still an open and challenging task, 1as discriminative feature is hard to learn from LR image with blurry appearance. To address this problem, many face super-resolution (SR) methods have been proposed to reconstruct high-resolution (HR) face images from the observed LR images. However, most of existing methods have two drawbacks: they tend to generate over-smoothed results, and they largely ignore to recover the facial identity information. In this paper, we propose a new multi-task framework for very small face image SR and recognition. First, we propose the Dense Up-down Sampling Unit (DUSU), which is able to effectively represent the nonlinear LR-to-HR mapping via an error-correcting feedback mechanism. Secondly, we introduce an effective and robust facial prior knowledge, the high frequency sub-bands of Non-Subsampled Contourlet Transform (NSCT), to enhance the texture details of super-resolved images. Third, we introduce an evaluation network with perceptual loss to recover the identity information of reconstructed face images. Extensive experiments demonstrate that our method not only achieves more appealing results than the state-of-the-art methods in terms of traditional SR metrics, but also significantly improve recognition accuracy of very small faces in the wild.
Through comparison with most frequency-domain digital watermarking approaches, the spread transform dither modulation (STDM) method exhibits a good embedding effect and is capable of meeting the requirements of blind extraction. However, the rotating attack is difficult to counter, and the cropping attack is seriously impacted by location factors. In addition, there exist numerous algorithms concentrate solely on the watermarking process, ignoring the image’s security. In these views, our work proposes a two-dimensional hyperchaotic system, the so-named Chebyshev ICMIC cascade map with the Knuth–Durstenfeld method, aiming to enhance the security of digital watermarking systems. The scrambled pixels improve the anticropping attack effect through breaking the location correlation of watermark information. For the purpose of magnifying resistance to rotation attack impact, the radial harmonic Fourier moments subtraction coefficient matrix distribution expectation is appropriately adapted to carry out the rotation correction. The performed experiments reveal that the suggested improved STDM watermarking algorithm demonstrates better robustness and invisibility, resisting more attacks than the existing methodologies. Finally, the feasibility of the proposed methods is proved based on the simulation results.
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