Cross-view geo-localization aims to match images of the same target from different platforms, e.g ., drone and satellite. It is a challenging task due to the changing appearance of targets and environmental content from different views. Most methods focus on obtaining more comprehensive information through feature map segmentation, while inevitably destroying the image structure, and are sensitive to the shifting and scale of the target in the query. To address the above issues, we introduce simple yet effective part-based representation learning, shifting-dense partition learning (SDPL). We propose a dense partition strategy (DPS), dividing the image into multiple parts to explore contextual information while explicitly maintaining the global structure. To handle scenarios with non-centered targets, we further propose the shifting-fusion strategy, which generates multiple sets of parts in parallel based on various segmentation centers, and then adaptively fuses all features to integrate their anti-offset ability. Extensive experiments show that SDPL is robust to position shifting, and performs competitively on two prevailing benchmarks, University-1652 and SUES-200. In addition, SDPL shows satisfactory compatibility with a variety of backbone networks ( e.g. , ResNet and Swin). https://github.com/C-water/SDPL_release.

Bokeh rendering is a technique used to take pictures with out-of-focus areas to highlight regions of interest. Due to limitations in hardware and shooting condition, rendering a bokeh image from a full-focus image has attracted a lot of interest. In this paper, we model bokeh rendering as the combination of salient region retention and bokeh blurring, and propose a neural network to generate a realistic bokeh image from a single full-focus image through end-to-end training. Specifically, we propose a gate fusion block to estimate the salient area, and introduce a constrained predictive filter for salient region retention and bokeh blurring within a unified architecture. Further, we utilize a pixel coordinate-based map to enhance the training. Experimental results illustrate the effectiveness of our model. The comparison with state-of-the-art methods (PyNET [1], DMSHN [2], BGGAN [3], etc.) shows that our model produces better bokeh effects and retains salient objects.

The recent imaging technology Quad Bayer CFA brings better imaging PSNR and higher visual quality compared to traditional Bayer CFA, but also serious challenges for demosaicing and denoising during the ISP pipeline. In this paper, we propose a novel dual encoder network, namely DRNet, to achieve joint demosaicing and denoising for Quad Bayer CFA. The dual encoders are carefully designed in that one is mainly constructed by a joint residual block to jointly estimate the residuals for demosaicing and denoising separately. In contrast, the other one is started with a pixel modulation block which is specially designed to match the characteristics of Quad Bayer pattern for better feature extraction. We demonstrate the effectiveness of each proposed component through detailed ablation investigations. The comparison results on public benchmarks illustrate that our DRNet achieves an apparent performance gain~(0.38dB to the 2nd best) from the state-of-the-art method and balances performance and efficiency well. The experiments on real-world images show that the proposed method could enhance the reconstruction quality from the native ISP algorithm.

Vision-based aerial-view geo-localization aims to match drone- and satellite-views of the same geographical location. Several feature partition strategies divide spatial features to mine contextual information. However, the compression from fine-grained features to visual descriptors is ill-considered, that is, classical pooling destroys discriminative features while increasing the sensitivity of networks to contextual information. In order to clarify this, we first review existing pooling layer and analyze their pros and cons when applied in feature compression. Inspired by the appearance of aerial views, we then summarize an ideal feature compression operation, i.e., precisely highlighting the central target while maximizing the use of environmental information in a feature-smoothing manner. To achieve the above process, we propose a distance-dependent parameter initialization strategy and form a novel pooling called D 2 -GeM pooling, which can explicitly guide the network to compress fine-grained features in multiple patterns. Extensive experiments on public benchmark University-1652 substantiate that our strategy attains more appealing results without additional costs

Moiré patterns usually depend on the style of display grids and the position of shooting camera, appearing in the form of stripes, meshes or ripples, with various and irregular colors. Compared with low-resolution moiré images, high-definition (HD) and ultra-high-definition (UHD) moiré images exhibit more complex moiré patterns, e.g., wider distribution of moiré frequencies and higher coupling degree of moirés of different scales, which poses a greater challenge to the modeling capabilities of the model. To address these challenges, we propose a novel Pyramid Learnable Bandpass Filtering Network (PBNet) for demoiréing UHD images. Specifically, we propose a pyramid learnable bandpass filter (P-LBF) to perform multi-scale filtering in the same semantic context to obtain richer frequency domain information. The P-LBF contains three stages: aligning, filtering and fusing. First, we introduce a pyramid alignment (DA) to align neighbor pixels for eliminating the deviations raised by different styles of display grids and relative position of the shooting camera. Then, a pyramid filtering (PF) is conducted to model the complex and variable moiré patterns with aligned neighbor pixels. Finally, the frequency domain responses of these different scales are fused with a multi-dimensional feature fusion (MFF). The PBNet is constructed based on the P-LBF, incorporating a cross-layer feature fusion (CLF) module to facilitate more effective information interaction between features at different depths. Extensive experiments on four public datasets show that our model achieves state-of-the-art performance for both high-and low-resolution moiré images. The code is publicly available at: https://github.com/liuzhongqi1/PBNet.