In recent years, deep multi-agent reinforcement learning (MARL) has demonstrated remarkable potential in solving complex cooperative tasks by enabling decentralized yet efficient coordination among agents. However, during decentralized training, agent policy updates induced by different joint action samples may conflict, leading to gradient interference that hinders convergence and the emergence of coordinated behavior. In this paper, we analyze and empirically validate the phenomenon of gradient interference. To address this, we then propose Gradient-Protected Value Decomposition (GPVD), a novel MARL framework that explicitly protects the gradient signals of optimal collaborative actions by suppressing the impact of interfering actions. GPVD employs a dynamic gradient protection mechanism that identifies optimal collaborative joint actions and reweights the loss to attenuate gradients from non-collaborative interfering actions. To effectively identify high-value collaborative actions, we apply SimHash-based state grouping to discover consistent collaboration patterns across similar states. Furthermore, a count-based intrinsic reward is incorporated to encourage exploration and improve the coverage of potentially optimal joint actions. Experiments on challenging multi-agent benchmarks demonstrate that GPVD achieves faster convergence, stronger coordination, and greater training stability compared to state-of-the-art value decomposition methods.

Diffusion-based image compression methods have achieved notable progress, delivering high perceptual quality at low bitrates. However, their practical deployment is hindered by significant inference latency and heavy computational overhead, primarily due to the large number of denoising steps required during decoding. To address this problem, we propose a diffusion-based image compression method that requires only a single-step diffusion process, significantly improving inference speed. To enhance the perceptual quality of reconstructed images, we introduce a discriminator that operates on compact feature representations instead of raw pixels, leveraging the fact that features better capture high-level texture and structural details. Experimental results show that our method delivers comparable compression performance while offering a 46× faster inference speed compared to recent diffusion-based approaches. The source code and models are available at https://github.com/cheesejiang/OSDiff.

Image compression at extremely low bitrates (below 0.1 bits per pixel (bpp)) is a significant challenge due to substantial information loss. In this work, we propose a novel two-stage extreme image compression framework that exploits the powerful generative capability of pre-trained diffusion models to achieve realistic image reconstruction at extremely low bitrates. In the first stage, we treat the latent representation of images in the diffusion space as guidance, employing a VAE-based compression approach to compress images and initially decode the compressed information into content variables. The second stage leverages pre-trained stable diffusion to reconstruct images under the guidance of content variables. Specifically, we introduce a small control module to inject content information while keeping the stable diffusion model fixed to maintain its generative capability. Furthermore, we design a space alignment loss to force the content variables to align with the diffusion space and provide the necessary constraints for optimization. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in terms of visual performance at extremely low bitrates. The source code and trained models are available athttps://github.com/huai-chang/DiffEIC.

Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that starts from pure noise, these methods are limited in both fidelity and efficiency. To address these two issues, we present Relay Residual Diffusion Extreme Image Compression (RDEIC), which leverages compressed feature initialization and residual diffusion. Specifically, we first use the compressed latent features of the image with added noise, instead of pure noise, as the starting point to eliminate the unnecessary initial stages of the denoising process. Second, we directly derive a novel residual diffusion equation from Stable Diffusion’s original diffusion equation that reconstructs the raw image by iteratively removing the added noise and the residual between the compressed and target latent features. In this way, we effectively combine the efficiency of residual diffusion with the powerful generative capability of Stable Diffusion. Third, we propose a fixed-step fine-tuning strategy to eliminate the discrepancy between the training and inference phases, thereby further improving the reconstruction quality. Extensive experiments demonstrate that the proposed RDEIC achieves state-of-the-art visual quality and outperforms existing diffusion-based extreme image compression methods in both fidelity and efficiency. The source code and pre-trained models are available at https://github.com/huai-chang/RDEIC.

Abstract Many learning‐based approaches to image deblurring have received increasing attention in recent years. However, the models trained on existing synthetic datasets do not generalize well to real‐world blur, resulting in undesirable artifacts and residual blur. This work attempts to address this problem from two aspects: training data synthesis and network architecture. To narrow the domain gap between synthetic and real domains, a realistic blur synthesis pipeline to generate high‐quality blurred data is proposed. Since the blur is non‐uniform and has different scales and degrees, a parallel feature complementary module to fully exploit the local and non‐local information, which improves the feature representation and helps the network to perceive the non‐uniform blur, is developed. In addition, a spatial Fourier reconstruction block to facilitate correct detail recovery in the spatial and Fourier domains is introduced. Based on these two designs, an effective encoder–decoder network for deblurring is designed. Extensive experiments demonstrate the validity and superiority of the proposed blur synthesis method and deblurring network. In particular, the proposed deblurring network can achieve superior or comparable performance to Restormer, while saving 70% of network parameters and 53% of floating point operations (FLOPs).

The goal of image rescaling is to embed the information from high-resolution images into low-resolution images and then reconstruct the high-resolution images in reverse. Existing methods either focus on small scaling factors or do not generalize well to natural images with diverse content in extreme settings, i.e., using extreme scaling factors (e.g., 16× and 32×). When performing extreme rescaling, previous methods often fail to produce plausible high-quality results due to insufficient cues in low-resolution images. In this work, we propose an extreme natural image rescaling framework that exploits the rich generative prior integrated into the GAN model trained on large-scale natural images to reduce the ambiguity of extreme upscaling. Considering the invertible bijective transformation between quantized features and low-resolution image, we develop an invertible feature recovery module that generates semantically sound low-resolution image while maximizing the preservation of useful features for the subsequent upscaling. Furthermore, we propose a multi-scale refinement module that explicitly introduces the supervised ground truth information to mitigate unpleasant artifacts and distortions. Extensive experiments show that the proposed rescaling framework formulated by the above components achieves significantly better visual performance than state-of-the-art methods.

Demand for full screen in consumer electronics has propelled the development of under-display image processing. For time-of-flight cameras, they are usually placed under TOLED display. Due to the presence of pixels and display patterns in the TOLED panel, depth maps from under-display time-of-flight (UD-ToF) are noisy, blurry, and inaccurate. We propose a non-local method based on Vision Transformer for UD-ToF depth restoration to address these issues. Specifically, a novel feature attention block is designed to incorporate non-local depth features. Additionally, we take ToF raw measurements rather than the depth map as input, allowing the network to extract informative features from the raw domain. We conduct comprehensive experiments on real RUD-TOF and synthetic SUD-TOF benchmark datasets, and the results indicate that the proposed transformer-based method achieves better performance than the state-of-the-art algorithms.

In this paper, we propose a two-branch deblurring framework. Given a blurred image, we first extract the edge map and employ an edge refinement network to recover the structure. Then the refined edge map is utilized to guide the subsequent deblurring process for correct structure recovery. Specifically, we develop a lightweight omni-dimensional attention module for long-range dependencies modeling and plug it into the edge refinement network, which effectively handles blur patterns with high variation. Furthermore, we propose a dynamic feature upsample module, which integrates dynamic convolution with upsampling and adaptively deals with the non-uniform blur. Extensive experiments show that our method outperforms state-of-the-art methods.

Abstract At present, attacks based on the vulnerability of the controller and flooding attacks still constitute a principal threat for hierarchical Software Defined Network (SDN), such as flow table tampering, malicious Application attacks, Distributed Denial of Service (DDoS) etc., due to the limitation against attacks based on known or unknown vulnerabilities for traditional cyber defence technology. Therefore, this study proposes an active defence architecture based on Mimic Defence (MD)–Mimic Hierarchical SDN Framework (MHSDN). Then endogenous security of MHSDN is theoretically analysed. Simultaneously, the attack surface measurement of MD is innovatively proposed, further improving the security and usability measurement standards of the MD system. Finally, to speed up detection and reduce defence cost of DDoS, this research proposes the Random Forest Feature Extract (RFFE) and tolerable switch migration. Simulation shows that RFFE has achieved a faster detection speed at the cost of less detection accuracy, and MHSDN can better improve the reliability of hierarchical SDN.

Under-display imaging has recently received considerable attention in both academia and industry. As a variation of this technique, under-display ToF (UD-ToF) cameras enable depth sensing for full-screen devices. However, it also brings problems of image blurring, signal-to-noise ratio and ranging accuracy reduction. To address these issues, we propose a cascaded deep network to improve the quality of UD-ToF depth maps. The network comprises two subnets, with the first using a complex-valued network in raw domain to perform denoising, deblurring and raw measurements enhancement jointly, while the second refining depth maps in depth domain based on the proposed multi-scale depth enhancement block (MSDEB). To enable training, we establish a data acquisition device and construct a real UD-ToF dataset by collecting real paired ToF raw data. Besides, we also build a large-scale synthetic UD-ToF dataset through noise analysis. The quantitative and qualitative evaluation results on public datasets and ours demonstrate that the presented network outperforms state-of-the-art algorithms and can further promote full-screen devices in practical applications.

Since the rapid development of face recognition systems using 3D cameras, the public has demanded great safety regulations for these devices. As a closely related topic, multimodal face anti-spoofing (FAS) has become an indispensable part of face recognition systems. However, existing multimodal FAS tools suffer from performance degradation under external low-lighting conditions and insufficient representation capabilities of fusion features. To address these issues, we present an attention-aware dual-stream fusion method using 3D cameras (i.e., IR+Depth) and considering both fine-grained and global features. Specifically, we introduce a surface normal generator using depth maps to obtain robust and discriminative representations. Then, we leverage the attention mechanism to split each stream into two branches. The first branch explores complementary global information between different modalities, while the second branch captures subtle and local features from each modality. The system regards multimodal FAS as a fine-grained classification problem. Moreover, to ensure that local areas in the image do not overlap and belong to the same class, a joint loss function is developed and proven to further boost the performance of FAS. We extensively evaluate our proposed strategies on various multimodal databases, and the results show that when compared with current state-of-the-art multimodal methods, our framework achieves superior performance.

In this paper an algorithm is presented to extract the valid depth data and correct the values of flying pixels by using depth information and confidence image. An adaptive segmentation for the measured depth image is executed based on kernel density estimation and one-pass connected component labeling. Then a modified structure tensor is used to detect the invalid pixels and the flying pixels contained in the depth image. Finally these pixels are corrected with the bi-cubic interpolation method or selectively removed by voting operation. And also, the erroneous pixels are excluded with augmented confidence. Experimental results have demonstrated the effectiveness of our algorithm.

In this paper, seismic quality factor Q estimation from vertical seismic profile (VSP) data is discussed, by using continuous wavelet transform (CWT). We suppose that source signature is a general constant-phase wavelet which matches the real one better. Based on the CWT of a reference and a target recording, we derive the formula of frequency-independent Q estimation by the ratio of wavelet-domain peak amplitude of these two recordings. Wavelet-domain peak amplitude denotes the peak module of CWT of the recording with every fixed scale. The formula is related to the dominant frequency and standard deviation of source signature. And the Q estimation formula with impulse source can be considered as its specific case that the standard deviation of source approaches the infinity. The synthetic zero-offset VSP and real field tests demonstrate that in the condition of constant-phase source signature, estimated Q from our formula can provide more accurate information than that by the formula with impulse source.