Recently, deep learning-based demosaicing methods have shown promising results. However, there has been little research on designing CFAs that are well-suited for specific deep learning-based demosaicing methods. This...
详细信息
Recently, deep learning-based demosaicing methods have shown promising results. However, there has been little research on designing CFAs that are well-suited for specific deep learning-based demosaicing methods. This is because it is challenging to establish a relationship between deep learning-based demosaicing methods and the CFAs they employ. This contrasts with traditional CFA design methods, which targeted fixed, non-deep learning demosaicing methods that did not depend on data learning, making signal processing theory applicable to the design. In this paper, we propose an optimized color filter array(CFA) tailored for the Denoising Diffusion Null-space Model (DDNM) based demosaicing. We begin by demonstrating the application of the DDNM to the demosaicing problem and establish the conditions under which the DDNM can accurately recover the true colors from CFA images containing colored pixels. Based on this analysis, we propose a CFA pattern that significantly improves the likelihood of accurate color reconstruction using the DDNM-based demosaicing method. Then, we outline the training process for obtaining the optimal filter coefficient values for the proposed CFA pattern. Experimental findings demonstrate that the proposed CFA yields favorable results when paired with the DDNM-based demosaicing technique which surpass those achieved by other CFA patterns.
Discrete mereotopology (DM) is a first-order spatial logic that fuses together mereology (the theory of parthood relations) and topology to model discrete space. We show how a set of quasitopological functions defined...
详细信息
Discrete mereotopology (DM) is a first-order spatial logic that fuses together mereology (the theory of parthood relations) and topology to model discrete space. We show how a set of quasitopological functions defined within DM can be mapped to specific operators defined in mathematical morphology (MM) and easily implemented in scientific image processing programs. These functions provide the means to model topological properties of individual regions and spatial relations between them such as contact, overlap, and the relation of part to whole. DM not only extends the expressive power of image processing applications where mathematical morphology is used, but by functioning as a logic it also supplies the formal basis with which to prove the correctness of implemented algorithms as well as providing the computational basis to mechanically reason about segmented digital images using automated reasoning programs. In particular, we show how DM can supply a model-based and algorithmic context to the otherwise blind pixel-based image processing routines still dominating conventional imaging approaches. A number of worked examples drawn from the, histological domain are given, including segmentation of cells in culture, identifying basal cell layers from stratified epithelia sections, and cell sorting in blood smears.
Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize we...
详细信息
This paper reports a real application whose task was to recognize characters printed on metal ingots. The problem is that surface of ingots is very uneven - ingots are hot or cold, cut by rough instrument, the printin...
详细信息
This paper reports a real application whose task was to recognize characters printed on metal ingots. The problem is that surface of ingots is very uneven - ingots are hot or cold, cut by rough instrument, the printing machine can be worn down, etc. In this paper, we present two original recognition methods: the first one is based on application of mathematical fuzzy logic and the second one is based on representation of an image by a fuzzy-valued function. Results of these methods are compared with a simple neural network classifier and few other common methods. (C) 2014 Elsevier B.V. All rights reserved.
The efficiency of hierarchical and wavelet image compression methods is analyzed and compared. More specifically, hierarchical grid interpolation (HGI) is compared with JPEG-2000. The characteristics of both methods a...
详细信息
Procrustes superposition and the thin-plate spline, each principally developed within the context of discrete landmark data, can be combined in a novel adaptive filter for detecting localized group differences of outl...
详细信息
ISBN:
(纸本)0818673672
Procrustes superposition and the thin-plate spline, each principally developed within the context of discrete landmark data, can be combined in a novel adaptive filter for detecting localized group differences of outline shape.
This article introduces a new theoretical framework to describe the behavior of the Steinbuch's Lernmatrix. The properties of this old associative memory can be modeled using set theory and order relationships, an...
详细信息
ISBN:
(纸本)0819459216
This article introduces a new theoretical framework to describe the behavior of the Steinbuch's Lernmatrix. The properties of this old associative memory can be modeled using set theory and order relationships, analogously to morphological associative memories. The obtained results allow the Lernmatrix, four decades before its creation, to be a good alternative for pattern classification and recognition.
Schistosomiasis is an epidemic disease that seriously endangers the health of people in China. Therefore, it is advantageous to screen for schistosome eggs automatically. This paper describes a screening method implem...
详细信息
Schistosomiasis is an epidemic disease that seriously endangers the health of people in China. Therefore, it is advantageous to screen for schistosome eggs automatically. This paper describes a screening method implemented on the Leitz TAS+ imageanalysis instrument for the presence of schistosome eggs in fecal samples.
We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A gro...
详细信息
We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A group average is an estimator that is said to best represent the common features of the group being studied. The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark through the process of template matching. Once the convergence criterion is satisfied numerically, the algorithms produce a group average and a local coordinate system for each member of the observing group, in terms of the residual momentum. We present several examples to illustrate the use of the proposed algorithms for finding a group average. Using the metrics computed between the group average and each member of the group, we successfully run a cluster analysis for datasets that contain a heavy percentage of outliers. Finally, we apply the collected residual momenta computed in the proposed algorithms in some statistical methods to demonstrate a potential application of the algorithms for detecting structure abnormality. (C) 2018 Elsevier Ltd. All rights reserved.
暂无评论