Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.

This study proposes a novel approach for detecting weak small infrared (IR) targets, called double-image and double-local contrast measurement (DDLCM), designed to overcome challenges of low contrast and complex backgrounds in images. In this approach, the original image is decomposed into odd and even images, and the gray difference contrast is determined using a dual-neighborhood sliding window structure, enhancing target saliency and contrast by increasing the distinction between the target and the local background. A central unit is then constructed to capture relationships between neighboring and non-neighboring units, aiding in clutter suppression and eliminating bright non-target interference. Lastly, the output value is derived by extracting the lowest contrast value of the weak small targets from the saliency map in each direction. Experimental results on two datasets demonstrate that the DDLCM algorithm significantly enhances real-time IR dim target detection, achieving an average performance improvement of 32.83%. The area under the ROC curve (AUC) decline is effectively controlled, with a maximum reduction limited to 3%. Certain algorithms demonstrate a notable AUC improvement of up to 43.96%. To advance infrared dim target detection research, we introduce the IFWS dataset for benchmarking and validating algorithm performance.

In the rapidly evolving image processing domain, transformers have emerged as powerful tools, yet significant challenges are encountered when they are applied to underwater image enhancement, such as visual disparity and computational inefficiency. Transformers do not have a unique module to maintain their performance while reducing the number of parameters. This study addresses the gap in the literature by introducing the globally deformable selection transformer (GS-Transformer), which is a model designed to enhance global feature selection and pixel connectivity, thereby reducing the computational complexity of the model while maintaining the image processing effect. Our novel multiresolution encoder-decoder module explicitly incorporates global information, overcoming the limitations of traditional transformers, whereas the multilocal coherence preserving loss (MCPL) mechanism ensures content integrity and coherence. Compared with the latest transform-based underwater image algorithms, this method is 15 times faster and utilizes only 41.7% (or approximately a half less) of the number of parameters. The experimental results on the UIEB, EUVP, and Synthesize datasets reveal that GS-Transformer achieves state-of-the-art performance in underwater image enhancement, with a reduced parameter number and improved efficiency, representing a significant advancement in the field. Our research will promote the application of the transformer in scenarios with high real-time performance.

The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.

A novel post-processing method, O2O (Online to Offline), to improve the result of shape retrieval is proposed in this paper. The essence of this proposed method is to move more work that consumes a lot of calculation to offline. With this essence, the O2O improves the retrieval result online with the help of the analysis offline. The offline analysis has two versions, one is self retrieval and the other is database clustering. The result of this kind of offline analysis can be reused indefinitely, regardless of the query shape, as long as the database is unchanged. Therefore, O2O is very efficient and suitable for real-time applications. This method is applied to shape retrieval and shape recognition with experiments on three databases, MPEG-7 CE1 Part B, Kimia 99 and Swedish Leaf. The results of the experiments in this paper show that O2O is not only efficient but also effective.

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability.