Interferometric Hyperspectral Imaging (IHI) is a critical technique for large-scale remote sensing tasks due to its advantages in flux and spectral resolution. However, IHI is susceptible to complex errors arising from imaging steps, and its quality is limited by existing signal processing-based reconstruction algorithms. Two key challenges hinder performance enhancement: 1) the lack of training datasets. 2) the difficulty in eliminating IHI-specific degradation components through learning-based methods. To address these challenges, we propose a novel IHI reconstruction pipeline. First, based on imaging physics and radiometric calibration data, we establish a simplified yet accurate IHI degradation model and a parameter estimation method. This model enables the synthesis of realistic IHI training datasets from hyperspectral images (HSIs), bridging the gap between IHI reconstruction and deep learning. Second, we design the Interferometric Hyperspectral Reconstruction Unfolding Transformer (IHRUT), which achieves effective spectral correction and detail restoration through a stripe-pattern enhancement mechanism and a spatial-spectral transformer architecture. Experimental results demonstrate the superior performance and generalization capability of our method.The code and are available at https://github.com/bit1120203554/IHRUT.

Event cameras are innovative neuromorphic sensors that asynchronously capture the scene dynamics. Due to the event-triggering mechanism, such cameras record event streams with much shorter response latency and higher intensity sensitivity compared to conventional cameras. On the basis of these features, previous works have attempted to reconstruct high dynamic range (HDR) videos from events, but have either suffered from unrealistic artifacts or failed to provide sufficiently high frame rates. In this paper, we present a recurrent convolutional neural network that reconstruct high-speed HDR videos from event sequences, with a key frame guidance to prevent potential error accumulation caused by the sparse event data. Additionally, to address the problem of severely limited real dataset, we develop a new optical system to collect a real-world dataset with paired high-speed HDR videos and event streams, facilitating future research in this field. Our dataset provides the first real paired dataset for event-to-HDR reconstruction, avoiding potential inaccuracies from simulation strategies. Experimental results demonstrate that our method can generate high-quality, high-speed HDR videos. We further explore the potential of our work in cross-camera reconstruction and downstream computer vision tasks, including object detection, panoramic segmentation, optical flow estimation, and monocular depth estimation under HDR scenarios.

Image denoising has progressed significantly due to the development of effective deep denoisers. To improve the performance in real-world scenarios, recent trends prefer to formulate superior noise models to generate realistic training data, or estimate noise levels to steer non-blind denoisers. In this paper, we bridge both strategies by presenting an innovative noise estimation and realistic noise synthesis pipeline. Specifically, we integrates a fine-grained statistical noise model and contrastive learning strategy, with a unique data augmentation to enhance learning ability. Then, we use this model to estimate noise parameters on evaluation dataset, which are subsequently used to craft camera-specific noise distribution and synthesize realistic noise. One distinguishing feature of our methodology is its adaptability: our pre-trained model can directly estimate unknown cameras, making it possible to unfamiliar sensor noise modeling using only testing images, without calibration frames or paired training data. Another highlight is our attempt in estimating parameters for fine-grained noise models, which extends the applicability to even more challenging low-light conditions. Through empirical testing, our calibration-free pipeline demonstrates effectiveness in both normal and low-light scenarios, further solidifying its utility in real-world noise synthesis and denoising tasks.

Low-light scenes are prevalent in real-world applications (e.g. autonomous driving and surveillance at night). Recently, multi-object tracking in various practical use cases have received much attention, but multi-object tracking in dark scenes is rarely considered. In this paper, we focus on multi-object tracking in dark scenes. To address the lack of datasets, we first build a Low-light Multi-Object Tracking (LMOT) dataset. LMOT provides well-aligned low-light video pairs captured by our dual-camera system, and high-quality multi-object tracking annotations for all videos. Then, we propose a low-light multi-object tracking method, termed as LTrack. We introduce the adaptive low-pass downsample module to enhance low-frequency components of images outside the sensor noises. The degradation suppression learning strategy enables the model to learn invariant information under noise disturbance and image quality degradation. These components improve the robustness of multi-object tracking in dark scenes. We conducted a comprehensive analysis of our LMOT dataset and proposed LTrack. Experimental results demonstrate the superiority of the proposed method and its competitiveness in real night low-light scenes. Dataset and Code: https:/github.com/ying-fu/LMOT

Most existing methods for over-exposure in image correction are developed based on sRGB images, which can result in complex and non-linear degradation due to the image signal processing pipeline. By contrast, data-driven approaches based on RAW image data offer natural advantages for image processing tasks. RAW images, characterized by their near-linear correlation with scene radiance and enriched information content due to higher bit depth, demonstrate superior performance compared to sRGB-based techniques. Further, the spectral sensitivity characteristics intrinsic to digital camera sensors indicate that the blue and red channels in a Bayer pattern RAW image typically encompass more contextual information than the green channels. This property renders them less susceptible to over-exposure, thereby making them more effective for data extraction in high dynamic range scenes. In this paper, we introduce a Channel-Guidance Network (CGNet) that leverages the benefits of RAW images for over-exposure correction. The CGNet estimates the properly-exposed sRGB image directly from the over-exposed RAW image in an end-to-end manner. Specifically, we introduce a RAW-based channel-guidance branch to the U-net-based backbone, which exploits the color channel intensity prior of RAW images to achieve superior over-exposure correction performance. To further facilitate research in over-exposure correction, we present synthetic and real-world over-exposure correction benchmark datasets. These datasets comprise a large set of paired RAW and sRGB images across a variety of scenarios. Experiments on our RAW-sRGB datasets validate the advantages of our RAW-based channel guidance strategy and proposed CGNet over state-of-the-art sRGB-based methods on over-exposure correction. Our code and dataset are publicly available athttps://github.com/whiteknight-WJN/CGNet.

Hyperspectral image (HSI) classification is a classical task in remote sensing image analysis. With the development of deep learning, schemes based on deep learning have gradually become the mainstream of HSI classification. However, existing HSI classification schemes either lack the exploration of category-specific information in the spectral bands and the intrinsic value of information contained in features at different scales, or are unable to extract multiscale spatial information and global spectral properties simultaneously. To solve these problems, in this article, we propose a novel HSI classification framework named CL-MGNet, which can fully exploit the category-specific properties in spectral bands and obtain features with multiscale spatial information and global spectral properties. Specifically, we first propose a spectral weight learning (SWL) module with a category consistency loss to achieve the enhancement of information in important bands and the mining of category-specific properties. Then, a multiscale backbone is proposed to extract the spatial information at different scales and the cross-channel attention via multiscale convolution and a grouping attention module. Finally, we employ an attention multilayer perceptron (attention-MLP) block to exploit the global spectral properties of HSI, which is helpful for the final fully connected layer to obtain the classification result. The experimental results on five representative hyperspectral remote sensing datasets demonstrate the superiority of our method.

High dynamic range (HDR) images capture much more intensity levels than standard ones. Current methods predominantly generate HDR images from 8-bit low dynamic range (LDR) sRGB images that have been degraded by the camera processing pipeline. However, it becomes a formidable task to retrieve extremely high dynamic range scenes from such limited bit-depth data. Unlike existing methods, the core idea of this work is to incorporate more informative Raw sensor data to generate HDR images, aiming to recover scene information in hard regions (the darkest and brightest areas of an HDR scene). To this end, we propose a model tailor-made for Raw images, harnessing the unique features of Raw data to facilitate the Raw-to-HDR mapping. Specifically, we learn exposure masks to separate the hard and easy regions of a high dynamic scene. Then, we introduce two important guidances, dual intensity guidance, which guides less informative channels with more informative ones, and global spatial guidance, which extrapolates scene specifics over an extended spatial domain. To verify our Raw-to-HDR approach, we collect a large Raw/HDR paired dataset for both training and testing. Our empirical evaluations validate the superiority of the proposed Raw-to-HDR reconstruction model, as well as our newly captured dataset in the experiments.

With the emergence of powerful deep learning tools, more and more effective deep denoisers have advanced the field of image denoising. However, the huge progress made by these learning-based methods severely relies on large-scale and high-quality noisy/clean training pairs, which limits the practicality in real-world scenarios. To overcome this, researchers have been exploring self-supervised approaches that can denoise without paired data. However, the unavailable noise prior and inefficient feature extraction take these methods away from high practicality and precision. In this paper, we propose a Denoise-Corrupt-Denoise pipeline (DCD-Net) for self-supervised image denoising. Specifically, we design an iterative training strategy, which iteratively optimizes the denoiser and noise estimator, and gradually approaches high denoising performances using only single noisy images without any noise prior. The proposed self-supervised image denoising framework provides very competitive results compared with state-of-the-art methods on widely used synthetic and real-world image denoising benchmarks.

Image denoising has achieved unprecedented progress as great efforts have been made to exploit effective deep denoisers. To improve the denoising performance in real-world, two typical solutions are used in recent trends: devising better noise models for the synthesis of more realistic training data, and estimating noise level function to guide non-blind denoisers. In this work, we combine both noise modeling and estimation, and propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation. Specifically, our model learns a noise estimation model with fine-grained statistical noise model in a contrastive manner. Then, we use the estimated noise parameters to model camera-specific noise distribution, and synthesize realistic noisy training data. The most striking thing for our work is that by calibrating noise models of several sensors, our model can be extended to predict other cameras. In other words, we can estimate camera-specific noise models for unknown sensors with only testing images, without laborious calibration frames or paired noisy/clean data. The proposed pipeline endows deep denoisers with competitive performances with state-of-the-art real noise modeling methods.

Event cameras are novel sensors that capture the dynamics of a scene asynchronously. Such cameras record event streams with much shorter response latency than images captured by conventional cameras, and are also highly sensitive to intensity change, which is brought by the triggering mechanism of events. On the basis of these two features, previous works attempt to reconstruct high speed and high dynamic range (HDR) videos from events. However, these works either suffer from unrealistic artifacts, or cannot provide sufficiently high frame rate. In this paper, we present a convolutional recurrent neural network which takes a sequence of neighboring events to reconstruct high speed HDR videos, and temporal consistency is well considered to facilitate the training process. In addition, we setup a prototype optical system to collect a real-world dataset with paired high speed HDR videos and event streams, which will be made publicly accessible for future researches in this field. Experimental results on both simulated and real scenes verify that our method can generate high speed HDR videos with high quality, and outperform the state-of-the-art reconstruction methods.