The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals’ facial data. This presents a notable paradox: while there is a societal demand for a robust face recognition ecosystem to ensure public security and convenience, an increasing number of individuals are hesitant to release their facial data. Numerous studies have endeavored to find such a utility-privacy trade-off, yet many struggle with the dilemma of prioritizing one at the expense of the other. In response to this challenge, this paper proposes PRO-Face C, a novel paradigm for privacy-preserving recognition of obfuscated faces via a dedicated feature compensation mechanism, aimed at optimizing the equilibrium between privacy preservation and utility maximization. The proposed approach is characterized by a specialized client-server architecture: the client transmits only obfuscated images to the server, which then performs identity recognition using a pre-trained model in conjunction with a suite of privacy-free complementary features. This framework facilitates accurate face identification while safeguarding the original facial appearance from explicit disclosure. Furthermore, the obfuscated image retains its visualization capability, crucial for image preview functionalities. To ensure the desired properties, we have developed an identity-guided feature compensation mechanism, complemented by several privacy-enhancing techniques. Extensive experiments conducted across multiple face datasets underscore the effectiveness of the proposed approach in diverse scenarios.

This paper presents a fresh paradigm for protecting facial privacy via an invertible image obfuscation framework that incorporates multiple characteristics including anonymity, diversity, reversibility, security, and lightweight all at once. We name the framework PRO-Face S, an acronym for Privacy-preserving Reversible Obfuscation of Face images via Secure flow. The core of the proposed framework is a flow-based generative model (or invertible neural network), which takes as input a face image along with its pre-obfuscated form, and outputs the privacy-protected image that visually mirrors the pre-obfuscated one. The pre-obfuscation applied can be in various forms with different types and strengths. The invertibility of the flow-based model ensures that the original image can be easily recovered from the protected image in high fidelity. An elaborate secret key mechanism is devised to securely guide the mutual transformations of privacy protection and image recovery, such that the correct recovery is only possible upon the availability of the correct secret, pre-specified by the user in the protection stage. Two modes of wrong recovery are investigated to deal with malicious recovery attempts in different scenarios. Finally, extensive experiments conducted on multiple image datasets demonstrate the superiority of the proposed framework over state-of-the-art methods.

Recently, remote sensing super-resolution (SR) tasks have been widely studied and achieved remarkable performance. However, due to the complex texture and serious image degeneration, the conventional methods (e.g. CNN-based, GAN-based) cannot reconstruct high-resolution (HR) remote sensing images with a large SR factor (≥ ×8). In this paper, we model the large-factor super-resolution (LFSR) task as a referenced diffusion process and explore how to embed pixel-wise constraint into the popular diffusion model. Following this motivation, we propose the first diffusion-based LFSR method named texture consistency diffusion model (TCDM) for remote sensing images. Specifically, we build a novel conditional truncated noise generator (CTNG) in TCDM to simultaneously generate the expectation of posterior probability p ( x _t-1 | x _t ) and the truncated noise image. With the predicted truncated noise image, sampling an SR image using CTNG saves nearly 90% processing time compared to the naive diffusion model. Additionally, we design a new denoising process named texture consistency diffusion (TC-diffusion) to explicitly embed pixel-wise constraints into the LFSR diffusion model during the training stage. Universal experiments on five commonly used remote sensing datasets demonstrate that the proposed TCDM surpasses the latest SR methods by a large margin and reports new SOTA results on several evaluation metrics. Additionally, the proposed method demonstrates impressive visual quality on reconstructed remote sensing image texture and details.

Recent CNN-driven face super-resolution (FSR) technologies have achieved excellent breakthroughs by incorporating facial prior knowledge. However, most of them suffer from some obvious limitations. They always estimate facial priors from input low-resolution (LR) faces or coarsely enhanced LR faces, obtaining unfaithful priors that cannot be adequately exploited. This may bring noticeable artifacts to the target results, especially for large scaling factors, deteriorating the fidelity and naturalness and generating suboptimal reconstructed results. In this paper, we propose a two-stage prior-guided FSR approach to learn facial prior knowledge from the optimal SR results of stage one and explore the complementarity between priors to further guide more accurate reconstruction in stage two. Specifically, we develop an efficient local and global interactive hybrid network incorporating facial semantic and geometric priors for more discriminative results. To reach this, we devise a multiscale interconnected symmetric encoder-decoder architecture composed of Prior Interaction-Integration Modules (PIIMs), the Coarse-to-fine Feature Refinement Module (CFRM), and Feature Aggregation Modulation Modules (FAMMs). The encoder concentrates on hierarchically extracting multiscale features. The CFRM is devised to explore the potential correlations between the encoder and the decoder and further guide the refinement and reinforcement of the encoded features. The decoder aims to take full advantage of informative multiscale encoded features to reconstruct high-quality SR representations. Comprehensive evaluation and visualization results on four benchmark datasets demonstrate the superiority of the proposed PLGNet over current state-of-the-art methods. The source code of PLGNet will be available at https://github.com/lil808/PLGNet.git.

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.