Infrared and visible image fusion can effectively integrate the advantages of two source images, preserving significant target information and rich texture details. However, most existing fusion methods are only designed for well-illuminated scenes and tend to lose details when encountering low-light scenes because of the poor brightness of visible images. Some methods incorporate a light adjustment module, but they typically focus only on enhancing intensity information and neglect the enhancement of color feature, resulting in unsatisfactory visual effects in the fused images. To address this issue, this paper proposes a novel method called EV-fusion, which explores the potential color and detail features in visible images and improve the visual perception of fused images. Specifically, an unsupervised image enhancement module is designed that effectively restores texture, structure and color information in visible images by several non-reference loss functions. Then, an intensity image fusion module is devised to integrate the enhanced visible image and the infrared image. Moreover, to improve the infrared salient object feature in the fused images, we propose an infrared bilateral-guided salience map embedding into the fusion loss functions. Extensive experiments demonstrate that our method outperforms state-of-the-art infrared visible image fusion methods.

Images captured in low-light conditions often suffer from bad visibility, e.g., low contrast, lost details, and color distortion, and image enhancement methods can be used to improve the image quality. Previous methods have generally obtained a smooth illumination map to enhance the image but have ignored details, leading to inaccurate illumination estimations. To solve this problem, we propose a multi-scale attention retinex network (MARN) for low-light image enhancement, which learns an image-to-illumination mapping to obtain a detailed inverse illumination map inspired by retinex theory. In order to introduce more image priors, we introduce a novel illuminance-attention map to guide the model to characterize varying-lighting areas, which we combine with the low-light image as the model input. MARN consists of a multi-scale attention module and a feature fusion module; the former extracts multi-resolution features with attention-based feature aggregation, while the latter further merges the output features of the previous module with the input. To achieve better visibility, we formulate a novel loss function to synthetically measure the illumination, detail, and colorfulness of the image. Extensive experiments are performed on several benchmark datasets. The results demonstrate that our method outperforms other state-of-the-art methods according to both objective and subjective metrics.

Event cameras provide high temporal resolution and high dynamic range and have application prospects in areas such as visual navigation, robotics, and image reconstruction. Event cameras output an asynchronous stream of address events that can be highly polluted by noise due to lighting and motion conditions. In order to better analyze and process noise with different spatiotemporal correlation, we introduce a method using an event address map (EAM) representation and classify event noise into background noise, near-edge noise, and recessive noise. In addition, since near-edge noise and recessive noise have a closer spatiotemporal correlation with real events, they are difficult to suppress with current denoising methods. Accordingly, we propose an adaptive EAM denoising method by adjusting the time window to form the EAM by measuring the number of events after pre-denoising and performing median filtering and opening. The method can better adapt to scenes of different motions to process near-edge noise, and can generate events to fill recessive noise. The output EAM can be directly used for subsequent process. Alternatively, to provide high temporal resolution, we restore the stream of address events from the output EAM. To suitably evaluate event denoising performance on real-world data, we introduce the average local event variance and amount of recessive noise, and the proposed method achieves improvements of 24.94% and 72.79%, respectively, compared with conventional denoising.

Intensified complementary metal-oxide semiconductor (ICMOS) sensors can capture images under extremely low-light conditions (≤0.01 lux illumination), but the results exhibit spatially clustered noise that seriously damages the structural information. Existing image-denoising methods mainly focus on simulated noise and real noise from normal CMOS sensors, which can easily mistake the ICMOS noise for the latent image texture. To solve this problem, we propose a low-light cross-scale transformer (LL-CSFormer) that adopts multi-scale and multi-range learning to better distinguish between the noise and signal in ICMOS sensing images. For multi-scale aspects, the proposed LL-CSFormer designs parallel multi-scale streams and ensures information exchange across different scales to maintain high-resolution spatial information and low-resolution contextual information. For multi-range learning, the network contains both convolutions and transformer blocks, which are able to extract noise-wise local features and signal-wise global features. To enable this, we establish a novel ICMOS image dataset of still noisy bursts under different illumination levels. We also designed a two-stream noise-to-noise training strategy for interactive learning and data augmentation. Experiments were conducted on our proposed ICMOS image dataset, and the results demonstrate that our method is able to effectively remove ICMOS image noise compared with other image-denoising methods using objective and subjective metrics.

Organic–inorganic hybrid perovskite (CH₃NH₃PbX₃, X = Cl, Br, or I) quantum dots have become one of the most promising materials for optoelectronic applications.