Push-broom imaging systems, including linear array (LA) and time delay integration (TDI) sensors, are extensively used in remote sensing for high-resolution image acquisition with continuous spatial coverage. However, platform-induced jitter introduces significant distortions and blurring, especially in TDI systems with multiple integration stages, where the cumulative effects of jitter are more pronounced. Traditional jitter models often struggle to accurately simulate these effects in the presence of measurement noise, hindering effective image restoration. In this article, we propose a novel jitter-aware restoration framework that addresses these challenges in both LA and TDI push-broom imaging systems. Central to our approach is the introduction of an equivalent jitter model (EJM) that is robust to measurement noise. By averaging time-shifted jitter curves across multiple integration stages, the EJM effectively smooths out noise-induced fluctuations, providing a reliable characterization of jitter effects. Leveraging this model, we develop a jitter-aware restoration network (JARNet), a two-stage restoration network that combines optical flow correction (OFC) with spatial-frequency residual learning to mitigate geometric distortions and motion blur. We also design a custom data synthesis pipeline to generate realistic jitter-degraded datasets, facilitating effective training of the network. Experimental results on both synthetic LA and TDI datasets demonstrate that JARNet outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and gradient magnitude similarity deviation (GMSD) metrics. Our framework offers a robust solution for restoring high-quality remote sensing images degraded by jitter, significantly advancing the state-of-the-art in this domain. The source code will be made publicly available upon publication athttps://github.com/naturezhanghn/EJM.

Linear Array Pushbroom (LAP) imaging technology is widely used in the realm of remote sensing. However, images acquired through LAP always suffer from distortion and blur because of camera jitter. Traditional methods for restoring LAP images, such as algorithms estimating the point spread function (PSF), exhibit limited performance. To tackle this issue, we propose a Jitter-Aware Restoration Network (JARNet), to remove the distortion and blur in two stages. In the first stage, we formulate an Optical Flow Correction (OFC) block to refine the optical flow of the degraded LAP images, resulting in pre-corrected images where most of the distortions are alleviated. In the second stage, for further enhancement of the pre-corrected images, we integrate two jitter-aware techniques within the Spatial and Frequency Residual (SFRes) block: 1) introducing Coordinate Attention (CoA) to the SFRes block in order to capture the jitter state in orthogonal direction; 2) manipulating image features in both spatial and frequency domains to leverage local and global priors. Additionally, we develop a data synthesis pipeline, which applies Continue Dynamic Shooting Model (CDSM) to simulate realistic degradation in LAP images. Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge. Extensive experiments demonstrate that the proposed two-stage method outperforms state-of-the-art image restoration models. Code is available at https://github.com/JHW2000/JARNet.

Video Frame Interpolation (VFI) is important for video enhancement, frame rate up-conversion, and slow-motion generation. The introduction of event cameras, which capture per-pixel brightness changes asynchronously, has significantly enhanced VFI capabilities, particularly for high-speed, nonlinear motions. However, these event-based methods encounter challenges in low-light conditions, notably trailing artifacts and signal latency, which hinder their direct applicability and generalization. Addressing these issues, we propose a novel per-scene optimization strategy tailored for low-light conditions. This approach utilizes the internal statistics of a sequence to handle degraded event data under low-light conditions, improving the generalizability to different lighting and camera settings. To evaluate its robustness in low-light condition, we further introduce EVFI-LL, a unique RGB+Event dataset captured under low-light conditions. Our results demonstrate state-of-the-art performance in low-light environments. Project page: https://naturezhanghn.github.io/sim2real.

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods and our proposed local blur-aware techniques are effective.

Time delay and integration cameras (TDI cameras) are widely used in remote sensing areas because they capture high-resolution, high signal-to-noise ratio (SNR) images and to image in low-light environments. However, the image quality captured by TDI cameras may be affected by many degradation factors, including jitter, charge transfer time mismatch and drift angle. Moreover, compared with the single-line push-broom cameras and area gaze cameras used in remote sensing, the degraded effect of the TDI camera may accumulate during the charge accumulation process. In this paper, we present a fast imaging simulation method for remote sensing TDI cameras based on image resampling that can accurately simulate the degraded image quality affected by different degradation factors. The simulated image pairs can provide a sufficient dataset for modern supervised-learning image restoration methods. In addition, we present a novel network, containing a row-attention block and row-encoder block to help resolve the row-variant blur to resolve the degraded images. We test our image restoration method on the simulated degraded image datasets and real images; the results show that the proposed method can effectively restore degraded images. Our restoration method does not rely on auxiliary information detected by high-frequency sensors or multispectral bands, and it achieves better results than other blind restoration methods.