Blind image deblurring, composed of estimating blur kernel and non-blind deconvolution, is an extremely ill-posed problem. However, previous deblurring methods still cannot solve delta kernel or noise problem well and avoid ringing artifacts in restored image, because the reliable kernel estimation and image restoration could not be given from information-deficiency input without using natural image priors. In this work, we find that the blur process changes the distribution of the image gradient, and thus attempt to use the priori knowledge for guiding blind deblurring. For the sake of convenience of modeling, we come up with a simplified approximate formulation of the image gradient distribution prior, thus the restoration model using it can be solved by the method of iteratively reweighted least squares (IRLS). We also concentrate on how to optimize the models and develop an algorithm based on gradient prior and image structure: first, we computed image structure based on the total variation model and adaptive weight strategy and then estimated the strong edges from it. Those strong edges (structure) that have a possible adverse effect on blur kernel estimation can be removed. Next, we followed an alternate iterative framework to obtain high-quality blur kernel estimation by estimating blur kernel from strong structure and then restoring a latent image guided by the formulated clear image priors. Finally, we proposed a non-blind deconvolution method based on fitted multi-order gradient priors as regularization to restore the latent image. In addition, we analyze the effectiveness of the knowledge-driven prior in image deblurring and demonstrate that it can favor clear images over blurred ones and restrain ringing artifacts effectively. Extensive experiments verify the superiority of the proposed method over state-of-the-art algorithms compared, both qualitatively and quantitatively.

Minutiae pattern extraction plays a crucial role in finger print registration and identification for electronic applications. However, the extraction accuracy is seriously compromised by the presence of contaminated ridge lines and complex background scenarios. General image processing-based methods, which rely on many prior hypotheses, fail to effectively handle minutiae extraction in complex scenarios. Previous works have shown that CNN-based methods can perform well in object detection tasks. However, the deep neural networks (DNNs)-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To address these challenges comprehensively, this paper presents a fully automated minutiae extraction method leveraging DNNs. Firstly, we create a fingerprint minutiae dataset using a semi-automated minutiae annotation algorithm. Subsequently, we propose a minutiae extraction model based on Residual Networks (Resnet) that enables end-to-end prediction of minutiae. Moreover, we introduce a novel non-maximal suppression (NMS) procedure, guided by the Generalized Intersection over Union (GIoU) metric, during the inference phase to effectively handle outliers. Experimental evaluations conducted on the NIST SD4 and FVC 2004 databases demonstrate the superiority of the proposed method over existing state-of-the-art minutiae extraction approaches.

While human spatial cognition relies on hierarchical abstraction to integrate local features, Alzheimer’s disease (AD) pathology is often reduced to local volumetric atrophy. We propose geometric fragility as a distinct failure of global topological integrity within the hippocampus rather than a mere accumulation of local errors. Using a texture-invariant point cloud framework, we contrasted local feature aggregation with global hierarchical attention as computational probes for structural perception. The global paradigm demonstrated enhanced diagnostic sensitivity and exhibited cognitive resilience under simulated neuronal sparsity by maintaining structural recognition where local models suffered pattern collapse. Global modeling effectively unfolded the pathological manifold, revealing a linear trajectory of geometric degradation consistently correlated with clinical cognitive decline. These findings indicate that AD progression may be characterized as a systemic topological dissolution and suggest that effective biomarkers should prioritize hierarchical shape abstraction to accurately map the continuous transition from healthy cognition to dementia.