Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields.

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.

The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target’s movement information, and the target’s detailed movement can be obtained using the event camera’s optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras.

The image decomposition strategy that extracts salient features from the source image is crucial for image fusion. To this end, we proposed a novel saliency-based decomposition strategy for infrared and visible image fusion. In particular, the latent low-rank representation (LatLRR) and rolling guidance filter (RGF) are together employed to process source images, which is called DLatLRR_RGF. In this method, the source images are first decomposed to salient components and base components based on LatLRR, and the salient components are filtered by RGF. Then, the final base components can be calculated by the difference between the source image and the processed salient components. The fusion rule based on the nuclear-norm and modified spatial frequency is used to fuse the salient components. The base components are fused by the l₂-energy minimization model. Finally, the fused image can be obtained by the fused base components and saliency detail components. Multiple groups of experiments on different pairs of infrared and visible images demonstrate that, compared with other state-of-the-art fusion algorithms, our proposed method possesses superior fusion performance from subjective and objective perspectives.

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.