Text-guided image style transfer methods have gradually become a research hotspot. However, existing text-guided style transfer method suffers from content information missing and artifacts in the generated stylized images. Therefore, we propose CReStyler, a text-guided image style method based on the dual-branch structure of CNN and Restormer. In this work, in the first branch, we introduce a novel convolutional structure called FcasNet, which composes Frequency-domain Channel Attention Mechanism (FcaNet) and Cross Convolutional Block Attention Module (CRCBAM). It can generate rough style images lfcaaccording to the input target text. In the second branch, the use of pixel-based Restormer constrains the phenomenon of fake images and content information missing due to excessive convolution. It can generate stylized images lreswith complete content information based on the target text. Finally, we combine lfcaand lresthrough weighted fusion to obtain refined stylized images. During the training process, we utilize directional CLIP loss to constrain text-image alignment. Experimental results show that our method produces better results compared with existing methods such as CLIPStyler, LDAST, Text2LIVE, InstructPix2Pix.

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model.

Qin Opera art is one of China’s intangible cultural heritage, and its influence is gradually declining. The cartoonization of Qin Opera is one of the feasible methods. However, current cartoonization methods suffer from the inability to classify and accurately cartoonize Qinqiang portraits by gender. Therefore, we propose Gender-Cartoon, which can achieve different gender portrait cartoons. The proposed method consists of four modules: gender classification, content feature extraction, gender identification style feature extraction and cartoonization. The gender classification module is used to obtain the gender labels of cartoon images, content feature module extracts content features from portraits by stacking convolutional blocks, and gender identification style extraction module uses the gender labels of cartoon images and image semantic features to obtain the corresponding gender style feature. The cartoonization module fuses content features and style features as the input of the adaptive residual block to obtain the corresponding cartoonization results. The experimental results show that the model can transform both texture and shape on the our collected Qinq Cartoon dataset Face2QinqCartoon and the public dataset Selfie2anime.

Most high-dimensional matrix recovery problems are studied under the assumption that the target matrix has certain intrinsic structures. For image data related matrix recovery problems, approximate low-rankness and smoothness are the two most commonly imposed structures. For approximately low-rank matrix recovery, the robust principal component analysis (PCA) is well-studied and proved to be effective. For smooth matrix problem, 2d fused Lasso and other total variation based approaches have played a fundamental role. Although both low-rankness and smoothness are key assumptions for image data analysis, the two lines of research, however, have very limited interaction. Motivated by taking advantage of both features, we in this paper develop a framework named projected robust PCA (PRPCA), under which the low-rank matrices are projected onto a space of smooth matrices. Consequently, a large class of image matrices can be decomposed as a low-rank and smooth component plus a sparse component. A key advantage of this decomposition is that the dimension of the core low-rank component can be significantly reduced. Consequently, our framework is able to address a problematic bottleneck of many low-rank matrix problems: singular value decomposition (SVD) on large matrices. Theoretically, we provide explicit statistical recovery guarantees of PRPCA and include classical robust PCA as a special case.