Single-image haze removal is challenging due to its ill-posed nature. The breadth of real-world scenarios makes it difficult to find an optimal dehazing approach that works well for various applications. This article addresses this challenge by utilizing a novel robust quaternion neural network architecture for single-image dehazing applications. The architecture’s performance to dehaze images and its impact on real applications, such as object detection, is presented. The proposed single-image dehazing network is based on an encoder-decoder architecture capable of taking advantage of quaternion image representation without interrupting the quaternion dataflow end-to-end. We achieve this by introducing a novel quaternion pixel-wise loss function and quaternion instance normalization layer. The performance of the proposed QCNN-H quaternion framework is evaluated on two synthetic datasets, two real-world datasets, and one real-world task-oriented benchmark. Extensive experiments confirm that the QCNN-H outperforms state-of-the-art haze removal procedures in visual quality and quantitative metrics. Furthermore, the evaluation shows increased accuracy and recall of state-of-the-art object detection in hazy scenes using the presented QCNN-H method. This is the first time the quaternion convolutional network has been applied to the haze removal task.

Real-world images captured in remote sensing, image or video retrieval, and outdoor surveillance are often degraded due to poor weather conditions, such as rain and mist. These conditions introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. In time-critical applications, it is vital to develop algorithms that automatically remove rain without compromising the quality of the image contents. This article proposes a novel approach called QSAM-Net, a quaternion multi-stage multiscale neural network with a self-attention module. The algorithm requires significantly fewer parameters by a factor of 3.98 than the real-valued counterpart and state-of-the-art methods while improving the visual quality of the images. The extensive evaluation and benchmarking on synthetic and real-world rainy images demonstrate the effectiveness of QSAM-Net. This feature makes the network suitable for edge devices and applications requiring near real-time performance. Furthermore, the experiments show that the improved visual quality of images also leads to better object detection accuracy and training speed.

Color constancy, the ability to perceive consistent object colors under varying illumination, is a core function of the human visual system and a persistent challenge in machine vision. Retinex theory models this process by decomposing an image $S$ into reflectance ($R$) and illumination ($I$) components ($S^{\prime }=RI$). However, conventional Retinex methods suffer from key limitations: independent RGB processing that disrupts inter-channel correlations, weak grounding in color perception models, non-invertible decomposition ($S^{\prime }\ne S$), and limited biological plausibility. We propose QRetinex-Net, a unified Retinex framework formulated in the quaternion domain—$S=R\otimes I$, where $\otimes$ denotes the Hamilton product. Representing RGB channels as pure quaternions enables holistic color processing, biologically inspired modeling, and invertible image reconstruction. We further introduce the Reflectance Consistency Index (RCI) to quantitatively assess illumination invariance and reflectance stability. Experiments on low-light crack detection, infrared–visible fusion, and face detection under varying lighting demonstrate that QRetinex-Net outperforms RetinexNet, KIND++, U-RetinexNet, and Diff-Retinex, achieving up to 11% performance gains, LPIPS ≈ 0.0001, and RCI ≈ 0.988.

Computer vision and deep learning are essential in diverse fields such as autonomous driving, medical imaging, face recognition, and object detection. However, enhancing low-light remote sensing images remains challenging for both research and real-world applications. Low illumination degrades image quality due to sensor limitations and environmental factors, weakening visual fidelity and reducing performance in vision tasks. Common issues such as insufficient lighting, backlighting, and limited exposure create low contrast, heavy shadows, and poor visibility, particularly at night. We propose QWR-Dec-Net, a quaternion-based Retinex decomposition network tailored for low-light image enhancement. QWR-Dec-Net consists of two key modules: a decomposition module that separates illumination and reflectance, and a denoising module that fuses a quaternion holistic color representation with wavelet multi-frequency information. This structure jointly improves color constancy and noise suppression. Experiments on low-light remote sensing datasets (LSCIDMR and UCMerced) show that QWR-Dec-Net outperforms current methods in PSNR, SSIM, LPIPS, and classification accuracy. The model’s accurate illumination estimation and stable reflectance make it well-suited for remote sensing tasks such as object detection, video surveillance, precision agriculture, and autonomous navigation.

Images used in real-world applications such as image or video retrieval, outdoor surveillance, and autonomous driving suffer from poor weather conditions. When designing robust computer vision systems, removing adverse weather such as haze, rain, and snow is a significant problem. Recently, deep-learning methods offered a solution for a single type of degradation. Current state-of-the-art universal methods struggle with combinations of degradations, such as haze and rain streaks. Few algorithms have been developed that perform well when presented with images containing multiple adverse weather conditions. This work focuses on developing an efficient solution for multiple adverse weather removal, using a unified quaternion neural architecture called CMAWRNet. It is based on a novel texture–structure decomposition block, a novel lightweight encoder–decoder quaternion transformer architecture, and an attentive fusion block with low-light correction. We also introduce a quaternion similarity loss function to better preserve color information. The quantitative and qualitative evaluation of the current state-of-the-art benchmarking datasets and real-world images shows the performance advantages of the proposed CMAWRNet, compared to other state-of-the-art weather removal approaches dealing with multiple weather artifacts. Extensive computer simulations validate that CMAWRNet improves the performance of downstream applications, such as object detection. This is the first time the decomposition approach has been applied to the universal weather removal task.

Wireless capsule endoscopy (WCE) offers a non-invasive diagnostic alternative for the gastrointestinal tract using a battery-powered capsule. Despite advantages, WCE encounters issues with video quality and diagnostic accuracy, often resulting in missing rates of 1–20%. These challenges stem from weak texture characteristics due to non-Lambertian tissue reflections, uneven illumination, and the necessity of color fidelity. Traditional Retinex-based methods used for image enhancement are suboptimal for endoscopy, as they frequently compromise anatomical detail while distorting color. To address these limitations, we introduce QRNet, a novel quaternion-based Retinex framework. QRNet performs image decomposition into reflectance and illumination components within hypercomplex space, maintaining inter-channel relationships that preserve color fidelity. A quaternion wavelet attention mechanism refines essential features while suppressing noise, balancing enhancement and fidelity through an innovative loss function. Experiments on Kvasir-Capsule and Red Lesion Endoscopy datasets demonstrate notable improvements in metrics such as PSNR (+2.3 dB), SSIM (+0.089), and LPIPS (−0.126). Moreover, lesion segmentation accuracy increases by up to 5%, indicating the framework’s potential for improving early-stage lesion detection. Ablation studies highlight the quaternion representation’s pivotal role in maintaining color consistency, confirming the promise of this advanced approach for clinical settings.