Camouflaged object detection (COD) is an arduous challenge due to the striking resemblance of camouflaged objects to their surroundings. The abundance of similar background information can significantly impede the efficiency of camouflaged object detection algorithms. Prior research in this domain has often relied on supplementary prior knowledge to guide model training. However, acquiring such prior knowledge is resource-intensive. Furthermore, the additional provided prior information is typically already embedded in the original image, but this information is underutilized. To address these issues, in this paper, we introduce a novel Camouflage Cues Guidance Network (CCGNet) for camouflaged object detection that does not rely on additional prior knowledge. Specifically, we use an adaptive approach to track the learning state of the model with respect to the camouflaged object and dynamically extract the cues of the camouflaged object from the original image. In addition, we introduce a foreground separation module and an edge refinement module to effectively utilize these camouflage cues, assisting the model in fully separating camouflaged objects and enabling precise edge prediction. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.

In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (${\mathrm{SA}}^3$) Network. The ${\mathrm{SA}}^3$ Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The ${\mathrm{SA}}^3$ Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model’s mAP under high IoU threshold conditions.

Remote sensing imagery is a crucial component of remote sensing data. However, in its application to downstream tasks, cloud cover can hinder effective data utilization, making the removal of cloud occlusion from remote sensing images a persistent and important research direction. Recently, diffusion models have demonstrated powerful performance in conditional image generation. However, their direct application to cloud removal yields suboptimal results, as the interference pattern of random Gaussian noise differs significantly from that of actual cloud occlusion. To address this, we developed the Perlin Noise-Based Cloud Removal Diffusion Model (PCRDiff). Compared to traditional diffusion models, PCRDiff abandons random Gaussian noise and instead utilizes Perlin noise to simulate the interference pattern of cloud occlusion on images. Based on this, we designed a novel training and iterative denoising process, along with a corresponding Perlin noise intensity quantization module. Furthermore, we developed a multi-attention fusion module as the backbone of the model to enhance its performance. Extensive experiments on two commonly used benchmark datasets demonstrate that our method achieves superior performance across multiple metrics.

Packet loss affects the received speech quality greatly in VoIP (Voice over Internet Protocol) applications. PCM (Pulse Code Modulation) coders are often used for VoIP and also face such problems. In this paper, a packet loss recovery algorithm is proposed based on CSI (Coding with Side Information) and Rate-Distortion optimization. Two virtual reconstruction methods are implemented for every voiced packet with side information at the sender endpoint. Rate-Distortion optimization is used to select the proper recovery method which has the lowest cost considering both the distortion and rate. At the receiver endpoint, the lost packets will be recovered with the selected recovery method. Simulation results show that the speech quality over IP network is improved dramatically at the price of only a low bandwidth consumption with the proposed method.

Image deblurring is an essential image preprocessing technique, aiming to recover clear and detailed images form blurry ones. However, existing algorithms often fail to effectively integrate multi-scale feature extraction with frequency enhancement, limiting their ability to reconstruct fine textures. Additionally, non-uniform blur in images also restricts the effectiveness of image restoration. To address these issues, we propose a multi-scale frequency enhancement network (MFENet) for blind image deblurring. To capture the multi-scale spatial and channel information of blurred images, we introduce a multi-scale feature extraction module (MS-FE) based on depthwise separable convolutions, which provides rich target features for deblurring. We propose a frequency enhanced blur perception module (FEBP) that employs wavelet transforms to extract high-frequency details and utilizes multi-strip pooling to perceive non-uniform blur, combining multi-scale information with frequency enhancement to improve the restoration of image texture details. Experimental results on the GoPro and HIDE datasets demonstrate that the proposed method achieves superior deblurring performance in both visual quality and objective evaluation metrics. Furthermore, in downstream object detection tasks, the proposed blind image deblurring algorithm significantly improves detection accuracy, further validating its effectiveness androbustness in the field of image deblurring.