Lang Wang

Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF. Approach. The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain’s sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction. Main results. Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations. Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

The species and population size of marine fish are important for maintaining the ecological environment and reflecting climate change. Traditional fish detection methods mainly rely on manual or traditional computer vision, which has disadvantages such as complex design, low detection accuracy, and poor generalization. The widespread use of ocean observation systems has accumulated a large number of images and videos, which makes the application of deep learning on marine fish detection possible. In this paper, a real-time high-precision fish detection algorithm based on YOLOv5s is constructed. Considering the enhancement of the channel representation and spatial interaction ability of the model, the attention mechanism and gated convolution are introduced, respectively, and GhostNet is introduced to lighten the model. Through a series of model comparisons, two improved models, S-Head-Ghost-Fish9 and S-SE-HorBlock-Head-Ghost-Fish9, are finally obtained. Compared with the original model, in terms of model size, the former reduces by 19% and the latter increases by 9.5%; in terms of computation, the former reduces by 15.7% and the latter reduces by 3.1%; in terms of detection speed, both take about 17 ms to detect a single image, and both can meet the real-time detection requirements; in terms of detection accuracy, the former improves by 3% and the latter by 3.6%. Compared with the latest detection algorithms of YOLOv6 and YOLOv8, the detection accuracy is slightly lower than 1%, but the model size and computation amount are only 1/3 to 1/2 of them. The improved models can help assess the population size and growth of the fish, which is of great significance in maintaining the stability of the fish population.