Recently, convolutional neural networks (CNNs) have achieved remarkable performance in SAR ship detection. However, intense background interference in SAR images and the inherent attributes of the ship targets severely hinder further improvements in detection accuracy. To address these problems, we propose a novel anchor-free detector with a salient feature pyramid network (SFPN) and an intersection-over-union (IoU) branch. First, SFPN aggregates multi-level semantic features and generates salient features for ship targets through a saliency extraction (SE) module. Then, a saliency fusion (SF) module merges the salient features and FPN features. In particular, we introduce a heatmap branch in SFPN as an extra supervision signal to direct the network to focus on salient regions containing ship targets rather than interference. Second, considering the shape characteristic that the length of a ship target is much larger than its width, we apply the IoU branch to the detection head to predict localization confidence for each detected bounding box. Extensive experiments conducted on High-Resolution SAR Images Dataset (HRSID) and SAR-Ship-Dataset demonstrate that the proposed method effectively improves detection accuracy and achieves state-of-the-art performance.

In this study, we propose a modeling-based compression approach for dense/lenslet light field images captured by Plenoptic 2.0 with square microlenses. This method employs the 5-D Epanechnikov Kernel (5-D EK) and its associated theories. Owing to the limitations of modeling larger image block using the Epanechnikov Mixture Regression (EMR), a 5-D Epanechnikov Mixture-of-Experts using Gaussian Initialization (5-D EMoE-GI) is proposed. This approach outperforms 5-D Gaussian Mixture Regression (5-D GMR). The modeling aspect of our coding framework utilizes the entire EI and the 5D Adaptive Model Selection (5-D AMLS) algorithm. The experimental results demonstrate that the decoded rendered images produced by our method are perceptually superior, outperforming High Efficiency Video Coding (HEVC) and JPEG 2000 at a bit depth below 0.06bpp.

Novel view synthesis from existing inputs remains a research focus in computer vision. Predicting views becomes more challenging when only a limited number of views are available. This challenge is commonly referred to as the few-shot view synthesis problem. Recently, various strategies have emerged for few-shot view synthesis, such as transfer learning, depth supervision, and regularization constraints. However, transfer learning relies on massive scene data, depth supervision is affected by input depth quality, and regularization causes increased computational costs or impaired generalization. To address these issues, we propose a new few-shot view synthesis framework called FewarNet that introduces trend regularization to leverage depth structural features and a warping loss to supervise depth estimation, possessing the advantages of existing few-shot strategies, enabling high-quality novel view prediction with generalization and efficiency. Specifically, FewarNet consists of three stages: fusion, warping, and rectification. In the fusion stage, a fusion network is introduced to estimate depths using scene priors from coarse depths. In the warping stage, the predicted depths are used to guide the warping of the input views, and a distance-weighted warping loss is proposed to correctly guide depth estimation. To further improve prediction accuracy, we propose trend regularization which imposes penalties on depth variation trends to provide depth structural constraints. In the rectification stage, a rectification network is introduced to refine occluded regions in each warped view to generate novel views. Additionally, a rapid view synthesis strategy that leverages depth interpolation is designed to improve efficiency. We validate the method’s effectiveness and generalization on various datasets. Given the same sparse inputs, our method demonstrates superior performance in quality and efficiency over state-of-the-art few-shot view synthesis methods.