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.

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.

Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.

Exposure correction aims to enhance visual data suffering from improper exposures, which can greatly improve satisfactory visual effects. However, previous methods mainly focus on the image modality, and the video counterpart is less explored in the literature. Directly applying prior image-based methods to videos results in temporal incoherence with low visual quality. Through thorough investigation, we find that the development of relevant communities is limited by the absence of a benchmark dataset. Therefore, in this paper, we construct the first real-world paired video dataset, including both underexposure and overexposure dynamic scenes. To achieve spatial alignment, we utilize two DSLR cameras and a beam splitter to simultaneously capture improper and normal exposure videos. Additionally, we propose an end-to-end video exposure correction network, in which a dual-stream module is designed to deal with both underexposure and overexposure factors, enhancing the illumination based on Retinex theory. The extensive experiments based on various metrics and user studies demonstrate the significance of our dataset and the effectiveness of our method. The code and dataset are available at https://github.com/kravrolens/VECNet.

Image-to-image (I2I) translation often requires establishing cycle consistency between the source and the translated images across different domains. However, cycle consistency requires redundant reconstruction, and is too restrictive to satisfy the bijection assumption between the two domains. In this paper, we propose SwinIT, a hierarchical Swin-transformer I2I Translation framework without using cycle consistency. Specifically, we carefully design symmetrical encoders for content and style flows, then explore newly proposed adaptive denormalization and normalization strategies. This framework can effectively capture and fuse content and style representations in a coarse-to-fine manner, ensuring our method achieves high performance without cycle consistency. Guided by element-wise feature adaptive denormalization, our model focuses on preserving semantic structure information. Due to the semantic mismatch between unpaired source and exemplar images, we introduce cross-attention adaptive instance normalization to help achieve better alignment. However, because the original optimization objective lacks direct supervision to preserve high-frequency information, rich edge details are lost during the translation. We propose a wavelet transformation matching loss to recover the details by converting the image into multi-frequency parts. We validate our proposed method in various I2I translation tasks, including arbitrary style transfer, multi-modal image synthesis, and semantic image synthesis, demonstrating its effectiveness in both qualitative and quantitative evaluations.

The objective of multi-domain image-to-image translation is to learn the mapping from a source domain to a target domain in multiple image domains while preserving the content representation of the source domain. Despite the importance and recent efforts, most previous studies disregard the large style discrepancy between images and instances in various domains, or fail to capture instance details and boundaries properly, resulting in poor translation results for rich scenes. To address these problems, we present an effective architecture for multi-domain image-to-image translation that only requires one generator. Specifically, we provide detailed procedures for capturing the features of instances throughout the learning process, as well as learning the relationship between the style of the global image and that of a local instance in the image by enforcing the cross-granularity consistency. In order to capture local details within the content space, we employ a dual contrastive learning strategy that operates at both the instance and patch levels. Extensive studies on different multi-domain image-to-image translation datasets reveal that our proposed method outperforms state-of-the-art approaches.