Non-local degradation modeling for spatially adaptive single image super-resolution.

Existing methods for single image super-resolution (SISR) model the blur kernel as spatially invariant across the entire image, and are susceptible to the adverse effects of textureless patches. To achieve improved results, adaptive estimation of the degradation kernel is necessary. We explore the synergy of joint global and local degradation modeling for spatially adaptive blind SISR. Our model, named spatially adaptive network for blind super-resolution (SASR), employs a simple encoder to estimate global degradation representations and a decoder to extract local degradation. These two representations are fused with a cross-attention mechanism and applied using spatially adaptive filtering to enhance the local image detail. Specifically, SASR contains two novel features: (1) a non-local degradation modeling with contrastive learning to learn global and local degradation representations, and (2) a non-local spatially adaptive filtering module (SAFM) that incorporates the global degradation and spatial-detail factors to preserve and enhance local details. We demonstrate that SASR can efficiently estimate degradation representations and handle multiple types of degradation. The local representations avoid the detrimental effect of estimating the entire super-resolved image with only one kernel through locally adaptive adjustments. Extensive experiments are performed to quantitatively and qualitatively demonstrate that SASR not only performs favorably for degradation estimation but also leads to state-of-the-art blind SISR performance when compared to alternative approaches.

Edge-Guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images.

Optical remote sensing images (RSIs) have been widely used in many applications, and one of the interesting issues about optical RSIs is the salient object detection (SOD). However, due to diverse object types, various object scales, numerous object orientations, and cluttered backgrounds in optical RSIs, the performance of the existing SOD models often degrade largely. Meanwhile, cutting-edge SOD models targeting optical RSIs typically focus on suppressing cluttered backgrounds, while they neglect the importance of edge information which is crucial for obtaining precise saliency maps. To address this dilemma, this article proposes an edge-guided recurrent positioning network (ERPNet) to pop-out salient objects in optical RSIs, where the key point lies in the edge-aware position attention unit (EPAU). First, the encoder is used to give salient objects a good representation, that is, multilevel deep features, which are then delivered into two parallel decoders, including: 1) an edge extraction part and 2) a feature fusion part. The edge extraction module and the encoder form a U-shape architecture, which not only provides accurate salient edge clues but also ensures the integrality of edge information by extra deploying the intraconnection. That is to say, edge features can be generated and reinforced by incorporating object features from the encoder. Meanwhile, each decoding step of the feature fusion module provides the position attention about salient objects, where position cues are sharpened by the effective edge information and are used to recurrently calibrate the misaligned decoding process. After that, we can obtain the final saliency map by fusing all position attention cues. Extensive experiments are conducted on two public optical RSIs datasets, and the results show that the proposed ERPNet can accurately and completely pop-out salient objects, which consistently outperforms the state-of-the-art SOD models.

A High-Sensitivity Dual-Axis Accelerometer with Two FP Cavities Assembled on Single Optical Fiber.

In this paper, a dual-axis Fabry-Pérot (FP) accelerometer assembled on single optical fiber is proposed. The sensor is equipped with a special beam-splitting prism to split the light into two perpendicular directions (the X- and Y-axes); the prism surface coated with semi-permeable film and the reflective sheet on the corresponding Be-Cu vibration-sensitive spring form two sets of FP cavities of different sizes. When the Be-Cu spring with a proof mass (PM) is subjected to the vibration signal, the cavity length of the corresponding FP cavity is changed and the interference signal returns to the collimator through the original path of the prism. After bandpass filtering and demodulation, the two cavity lengths are obtained, and the acceleration measurement in dual-axis directions is completed. The resonant frequency of the proposed dual-axis fiber optic accelerometer is around 280 Hz. The results of the spectral measurements show 3.93 µm/g (g = 9.8 m/s2: gravity constant) and 4.19 µm/g for the applied acceleration along the X- and Y-axes, respectively, and the cross-axis sensitivity is below 5.1%. Within the angle range of 180°, the maximum error of measured acceleration is less than 3.77%. The proposed fiber optic dual-axis FP accelerometer has high sensitivity and strong immunity to electromagnetic interference. The size of the sensor mainly depends on the size of the prism, which is easy to reduce and mass produce. Moreover, this FP construction method has high flexibility and development potential.

Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective.

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability are still not completely understood. Previous work mostly explores the causes from the model perspective, e.g., decision boundary, model architecture, and model capacity. Here, we investigate the transferability from the data distribution perspective and hypothesize that pushing the image away from its original distribution can enhance the adversarial transferability. To be specific, moving the image out of its original distribution makes different models hardly classify the image correctly, which benefits the untargeted attack, and dragging the image into the target distribution misleads the models to classify the image as the target class, which benefits the targeted attack. Towards this end, we propose a novel method that crafts adversarial examples by manipulating the distribution of the image. We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method. Our method can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios, surpassing the previous best method by up to 40% in some cases. In summary, our work provides new insight into studying adversarial transferability and provides a strong counterpart for future research on adversarial defense.

Rich Structural Index for Stereoscopic Image Quality Assessment.

The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.

Learning Frequency Domain Priors for Image Demoireing.

Image demoireing is a multi-faceted image restoration task involving both moire pattern removal and color restoration. In this paper, we raise a general degradation model to describe an image contaminated by moire patterns, and propose a novel multi-scale bandpass convolutional neural network (MBCNN) for single image demoireing. For moire pattern removal, we propose a multi-block-size learnable bandpass filters (M-LBFs), based on a block-wise frequency domain transform, to learn the frequency domain priors of moire patterns. We also introduce a new loss function named Dilated Advanced Sobel loss (D-ASL) to better sense the frequency information. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. To determine the most appropriate frequency domain transform, we investigate several transforms including DCT, DFT, DWT, learnable non-linear transform and learnable orthogonal transform. We finally adopt the DCT. Our basic model won the AIM2019 demoireing challenge. Experimental results on three public datasets show that our method outperforms state-of-the-art methods by a large margin.

Dynamic Selective Network for RGB-D Salient Object Detection.

RGB-D saliency detection is receiving more and more attention in recent years. There are many efforts have been devoted to this area, where most of them try to integrate the multi-modal information, i.e. RGB images and depth maps, via various fusion strategies. However, some of them ignore the inherent difference between the two modalities, which leads to the performance degradation when handling some challenging scenes. Therefore, in this paper, we propose a novel RGB-D saliency model, namely Dynamic Selective Network (DSNet), to perform salient object detection (SOD) in RGB-D images by taking full advantage of the complementarity between the two modalities. Specifically, we first deploy a cross-modal global context module (CGCM) to acquire the high-level semantic information, which can be used to roughly locate salient objects. Then, we design a dynamic selective module (DSM) to dynamically mine the cross-modal complementary information between RGB images and depth maps, and to further optimize the multi-level and multi-scale information by executing the gated and pooling based selection, respectively. Moreover, we conduct the boundary refinement to obtain high-quality saliency maps with clear boundary details. Extensive experiments on eight public RGB-D datasets show that the proposed DSNet achieves a competitive and excellent performance against the current 17 state-of-the-art RGB-D SOD models.