Arbitrary-scale video super-resolution (VSR) aims to enhance video resolution at continuous scales and has attracted increasing attention in recent years. However, existing methods typically rely on fixed degradation modes, such as bicubic downsampling, which often fail to handle the complex degradations of real-world videos. Current real-world datasets only cover limited scales (e.g., ×2, ×4) and are insufficient to capture the diverse degradations required for arbitrary-scale VSR. To address this, we present RealArbVSR, the first real-world VSR dataset with both integer and decimal scale factors, providing a wider range of degradation levels. Moreover, to generate continuous degradations beyond the collected scales, we propose the Continuous Degradation Generation Network (CDGN), which synthesizes realistic LR videos with arbitrary degradations. Specifically, we design a Scale-aware Degradation Module (SDM) to adaptively learn scale-specific degradations and an Implicit Filter Module (IFM) that represents spatial-temporal features as a continuous feature domain for arbitrary-scale LR frame generation. Extensive experiments demonstrate that our CDGN trained on RealArbVSR produces high-fidelity LR videos with arbitrary degradations and significantly enhances the performance of VSR models in real-world scenarios. The RealArbVSR dataset and source code will be publicly released for further research.

Exposure correction aims to restore underexposed and overexposed images to normal exposed images in a single network. However, conventional methods primarily focus on correcting non-extreme exposure cases and struggle to accurately restore lightness and structure information in extreme exposure scenarios. Through a thorough investigation, we observe that the extreme exposure correction task is limited by the lack of high-quality benchmark datasets. To address the above challenges, in this paper, we construct the first Extreme Exposure Dataset named EED by manually collecting a large number of diverse scenes. By introducing probabilistic blur kernel, EED not only ensures the rich diversity and brightness distribution of scenes but also approaches the degradation of the real world. To achieve exposure correction in extreme conditions, we propose a novel Extreme Exposure Correction Network by leveraging the mask-aware Fourier transform prior, which decouples lightness and structure components precisely. To restore severe abnormal lightness and lost structure information in extreme exposure scenes, we introduce a well-exposed referenced image to guide the coarse restoration and employ a Timestep-guided Frequency Diffusion Module for further refinement. Extensive experiments demonstrate the superiority of our dataset and method. The dataset will be available at https://github.com/juvenoia/EED.

Image downscaling is critical for efficient storage and transmission of high-resolution (HR) images. Existing learning-based methods focus on performing downscaling within the sRGB domain, which typically suffers from blurred details and unexpected artifacts. RAW images, with their unprocessed photonic information, offer greater flexibility but lack specialized downscaling frameworks. In this paper, we propose a wavelet-based recurrent reconstruction framework that leverages the information lossless attribute of wavelet transformation to fulfill the arbitrary-scale RAW image downscaling in a coarse-to-fine manner, in which the Low-Frequency Arbitrary-Scale Downscaling Module (LASDM) and the High-Frequency Prediction Module (HFPM) are proposed to preserve structural and textural integrity of the reconstructed low-resolution (LR) RAW images, alongside an energy-maximization loss to align high-frequency energy between HR and LR domain. Furthermore, we introduce the Realistic Non-Integer RAW Downscaling (Real-NIRD) dataset, featuring a non-integer downscaling factor of 1.3×, and incorporate it with publicly available datasets with integer factors (2×, 3×, 4×) for comprehensive benchmarking arbitrary-scale image downscaling purposes. Extensive experiments demonstrate that our method outperforms existing state-of-the-art competitors both quantitatively and visually. The code and dataset will be released at https://github.com/RenYangSCU/ASRD.

Event-based image reconstruction has achieved remarkable progress, benefiting from the high temporal resolution and high dynamic range of event cameras. However, most event-based methods focus on enhancing sRGB image quality, neglecting the potential of leveraging event data for RAW-to-sRGB conversion. Due to the limitations of camera sensors, images processed through standard ISP pipelines often suffer from motion blur and color distortion in dynamic scenes. In contrast, RAW images preserve uncompressed scene information, integrating event signals at this stage enables finer texture recovery and more accurate color correction. To tackle these challenges, we propose EvRAW, a novel event-assisted RAW-to-sRGB image reconstruction network that integrates event signals to promote high-fidelity sRGB image reconstruction. Specifically, we introduce a Motion-guided Structural Enhancement (MSE) module that extracts motion patterns from event streams and aggregates dynamic features to restore fine textures. Additionally, we propose an Adaptive Color Correction (ACC) module that performs region-wise gamma correction and channel-wise color decoding to enhance color fidelity under complex lighting conditions. To evaluate performance in challenging real-world scenarios, we collect a pixel-aligned RAW-Event dataset specifically for this task. Extensive experiments demonstrate that EvRAW achieves state-of-the-art performance in RAW-to-sRGB reconstruction on both synthetic and real-world datasets.

Low-light video enhancement is a critical task in computer vision with a wide range of applications. However, there is a lack of high-quality benchmark datasets in this field. To address this issue, we collect a high-quality low-light video dataset using a well-designed camera system. The videos in our dataset feature apparent camera motion and strict spatial alignment. In order to achieve general low-light video enhancement, we propose a Retinex-based method called Light Adjustable Network (LAN). LAN iteratively adjusts the brightness and adapts to different lighting conditions in various real-world scenarios, producing visually appealing results. We further develop a new dataset capture method and low-light video enhancement method to address the limitation of our previous dataset in capturing dynamic scenes and previous method. The new camera setup and capture method enable the recording of real continuous videos and generate the new dataset. Our new low-light video enhancement method, LAN++, leverages a new inter-frame relationship, difference images. It utilizes the texture information contained in the difference images of dynamic scenes to supplement the high-frequency details of the original features, which produce sharper and more realistic output images. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset can be downloaded at https://pan.baidu.com/s/1d3EljvVduVM0wUOvzjWaqA?pwd=p45g.

Due to the limitations of sensor, traditional cameras struggle to capture details within extremely dark areas of videos. The absence of such details can significantly impact the effectiveness of low-light video enhancement. In contrast, event cameras offer a visual representation with higher dynamic range, facilitating the capture of motion information even in exceptionally dark conditions. Motivated by this advantage, we propose the Real-Event Embedded Network for low-light video enhancement. To better utilize events for enhancing extremely dark regions, we propose an Event-Image Fusion module, which can identify these dark regions and enhance them significantly. To ensure temporal stability of the video and restore details within extremely dark areas, we design unsupervised temporal consistency loss and detail contrast loss. Alongside the supervised loss, these loss functions collectively contribute to the semi-supervised training of the network on unpaired real data. Experimental results on synthetic and real data demonstrate the superiority of the proposed method compared to the state-of-the-art methods.

Low-light video enhancement is a challenging task with broad applications. However, current research in this area is limited by the lack of high-quality benchmark datasets. To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras. Our dataset provides dynamic video pairs with pronounced camera motion and strict spatial alignment. To achieve general low-light video enhancement, we also propose a novel Retinex-based method named Light Adjustable Network (LAN). LAN iteratively refines the illumination and adaptively adjusts it under varying lighting conditions, leading to visually appealing results even in diverse real-world scenarios. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset is available at https://github.com/ciki000/DID.