State-of-the-art face hallucination methods utilize convolutional neural networks to restore high-quality high-resolution (HR) face images from low-resolution (LR) face images by employing appearance knowledge. However, most of these solutions only deploy the appearance knowledge (such as face attributes) to modulate the first layer of convolution feature with channel-wise shifting and ignore the face-identity information while emphasizing the vision quality of the generated result. To address the above issues, we propose a semantic-driven residual network based on a generative adversarial network to restore the HR face image with proper identity from the LR face image. Firstly, precise semantic information is explored based on improved U-net. Secondly, the semantic information is concatenated into the residual blocks of the reconstruction module, which is exceptionally efficient in modulating the extracted feature and guiding the generation of HR face images. Finally, the proposed methods can obtain the pleasuring SR face image with a similar identity of LR face image, after adversarially optimizing the designed loss functions. Extensive experiments demonstrate that the proposed semantic-driven residual network performs against the state-of-the-arts both quantitatively and qualitatively.

Most image deblurring methods require large algorithm computational cost because multi‐scale blind deconvolution is used for estimating kernel. Furthermore, a moving quick response (QR) code image is regarded as a type of classical blurry image and requires real‐time processing in practical applications. Therefore, this study proposes a new framework of motion blurry QR code image restoration in real‐time based on the fractional‐order deblurring method. The authors perform a trade‐off between algorithm computational cost and quality of the deblurring image. First, a black frame is added around the traditional QR code, which is used for locating QR code and reducing the computational cost. Next, a new image deblurring method is proposed using fractional differential order and is used for improving the quality of the deblurring image. Furthermore, an average grey‐level method is presented to reconstruct the standard QR code images. Comparisons with the existing algorithms demonstrate that the proposed method can achieve favourable deblurring quality and acceptable computational cost. Finally, their framework is validated in a practical platform of an actual conveyor belt system with a low‐cost industrial camera. Experimental results indicate that their framework performs favourably with processing motion blurry QR code images.

Rainy degeneration damages an image’s visual effect and influences the performance of subsequent vision tasks. Various deep learning methods for single image deraining have been proposed, obtaining appropriate recovery results. Unfortunately, most existing methods ignore the interaction between rain-layer and rain-free components when extracting relevant features, leading to undesirable results. To break the above limitations, we propose a progressive hybrid-modulated network (PHMNet) for single image deraining based on the two-branch and coarse-to-fine framework. Specifically, a hybrid-modulated module (HMM) with a two-branch framework is proposed to blend and modulate the feature of rain-free layers and rain streaks. After cascading several HMMs in the coarsest reconstructed stage of the PHMNet, a multi-level refined module (MLRM) is adopted to refine the final deraining results in the refined reconstructed stage. By being trained using loss functions such as contrastive learning, the PHMNet can obtain satisfactory deraining results. Extended experiments on several datasets and downstream tasks demonstrate that our method performs favorably against state-of-the-art methods in quantitative evaluation and visual effects.