Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies, and over-editing due to the lack of effective integration of source information. In this paper, we present FIA-Edit, a novel inversion-free framework that achieves high-fidelity and semantically precise edits through a Frequency-Interactive Attention. Specifically, we design two key components: (1) a Frequency Representation Interaction (FRI) module that enhances cross-domain alignment by exchanging frequency components between source and target features within self-attention, and (2) a Feature Injection (FIJ) module that explicitly incorporates source-side queries, keys, values, and text embeddings into the target branch's cross-attention to preserve structure and semantics. Comprehensive and extensive experiments demonstrate that FIA-Edit supports high-fidelity editing at low computational cost (~6s per 512 * 512 image on an RTX 4090) and consistently outperforms existing methods across diverse tasks in visual quality, background fidelity, and controllability. Furthermore, we are the first to extend text-guided image editing to clinical applications. By synthesizing anatomically coherent hemorrhage variations in surgical images, FIA-Edit opens new opportunities for medical data augmentation and delivers significant gains in downstream bleeding classification.

Image quality assessment (IQA) algorithms have significantly advanced over the past two decades, primarily focusing on natural images. However, applying these methods directly to medical imaging often yields suboptimal performance due to inherent differences such as the structural complexity of medical images and the limited availability of annotated databases. In this study, we conduct a comprehensive evaluation of state-of-the-art IQA methods, including 29 traditional full-reference (FR), 4 traditional no-reference (NR), and 9 deep learning-based approaches, to assess their effectiveness in the context of medical imaging. Our evaluation is performed on a recently developed MRI image quality assessment benchmark, revealing critical performance gaps in existing methods. Building on these findings, we propose a novel dual-branch deep learning framework specifically designed for medical IQA (MIQANet). The proposed approach effectively combines global contextual information with local structural details, enhancing the model’s ability to capture subtle degradations and structural inconsistencies in MRI scans. Experiential results demonstrate the superiority of our approach over existing methods, providing valuable theoretical and practical insights for enhancing quality assessment of medical images.

Radiologists’ search strategies and decision-making processes during chest X-ray diagnosis vary with their levels of expertise. Understanding these differences can inform training programmes and support the development of tools to enhance diagnostic accuracy. We hypothesize that eye-tracking data can reveal variations in expertise levels that serve as a predictor of radiologist expertise. To investigate this, we develop a database of 191 chest X-ray images with ground-truth annotations, including diagnostic decisions and eye movement patterns from 13 radiologists of varying levels of expertise. Statistical analyses reveal distinct diagnostic search patterns associated with different expertise levels. In addition, we propose a predictive framework to estimate expertise levels based on eye-tracking data. This study advances the understanding of expertise-driven differences in diagnostic search strategies and demonstrates the potential of eye-tracking data in enhancing training in clinical radiology.

Advancements in deep learning have led to significant progress in no-reference (NR) image quality assessment (NR-IQA) for evaluating the perceived quality of digital images without relying on a reference. However, existing NR-IQA models remain suboptimal in handling complex and diverse natural images. Visual saliency constitutes a critical element for enhancing the reliability of NR-IQA, but the optimal use of saliency in deep learning-based NR-IQA has not heretofore been significantly explored. In this article, we present a novel method for integrating saliency in NR-IQA, which is motivated by the saliency-based visual search mechanism that different parts of the visual input are visited by the focus of attention (FOA) in the order of decreasing saliency. By dividing saliency into the high and low levels of FOA, we build a bioinspired deep neural network-BioSIQNet-based on a multitask learning (MTL) framework. The network architecture consists of two saliency-specific tasks and one primary image quality assessment (IQA) task. The low and high saliency (HS) are separately encoded and integrated into the early and deeper layers of the IQA network, respectively, analogous to the hierarchical processing in the visual cortex of the brain that allocates low attentional resources to process the simple patterns and high resources to learn intricate representations. We demonstrate that leveraging the synergy between visual attention and image quality perception and joint learning of these interconnected visual tasks can enhance the overall learning capabilities of the primary IQA model. Experiments validate the effectiveness of our proposed BioSIQNet for NR-IQA.

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Low quality medical images can lead to inaccurate interpretation and diagnosis. Therefore, it is important to understand radiologists' perception of distortions in visual content. In this study, 12 radiologists with different degrees of experience scored MRI images of varying levels of quality. Statistical analyses were conducted to reveal the influence of the radiologists' experience on their perception of image quality. In scoring images of joints, brain and liver, the highly experienced radiologists gave significantly higher scores than less experienced radiologists. In scoring images of fetus and spine, there were no significant differences in scores between groups with different degrees of experience. No radiologists had expertise in breast images, and their experience in other anatomical areas did not significantly affect scoring of breast images. Overall, highly experienced radiologists gave higher scores for images with edge ghosting, plain ghosting or white noise than radiologists with less experience. The findings will provide a reference for determining or improving image quality standards in clinical practice.

Detecting small infrared (IR) targets against low-altitude complex background is always a challenge for IR search and tracking (IRST) system due to limited small target characteristics, the moving background caused by camera motion, and extremely cluttered backgrounds. The existing methods usually cause high false alarm or do not work against the chaotic low-altitude complex background. In this article, a novel spatial–temporal tensor model with saliency filter regularization (STTM-SFR) is developed to detect small IR targets. First, the small target detection task is transformed into a sparse and low-rank tensor optimization problem using the spatial–temporal prior knowledge of background and target. The construction of the holistic STTM can retain the complete spatial–temporal information of the original IR image sequence. Then, the SFR term limited between background and foreground aims to promote target saliency learning. That is to say, the SFR term can avoid the offset approximation of the low-rank tensor, so as to recover a clean target image from the original IR tensor. Finally, an effective alternating direction method of multipliers (ADMM) algorithm framework is designed to solve the proposed STTM-SFR model. The effectiveness and robustness of the STTM-SFR model are verified in six real IR scenes. Experimental results show that our method outperforms other baseline methods. Moreover, the proposed STTM-SFR method is more robust than the existing state-of-the-art STTMs against low-altitude moving backgrounds.

Infrared (IR) small target detection in the complex background is an important but challenging research hotspot in the field of target detection. The existing methods usually cause high false alarms in the complex background and fail to make full use of the complete information of the image. In this article, a novel IR small target detection model that combines facet derivative-based multidirectional edge awareness with spatial–temporal tensor (FDMDEA-STT) is presented. First, we construct an STT model (STTM) to transform the target detection problem into a low-rank and sparse tensor optimization problem based on the prior information of the target and background in the spatial–temporal domain. Then, based on the facet derivative, we define a multidirectional edge awareness mapping and fuse it into the STTM as sparse prior information. Finally, an effective algorithm based on the alternating direction method of multipliers (ADMM) is designed to solve the above model. The effectiveness of the proposed method is verified on eight real IR image sequences. Experimental results demonstrate that the proposed method has better detection performance than the existing state-of-the-art methods.