The Convolutional Sparse Coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional sparse coding methods, a significant assumption is that all input samples are independent, so it is not well for most dependent works. In ...
2. 研究“disentangled convolutional sparse coding”的原理及应用 解耦卷积稀疏编码是一种将图像特征分解为多个独立且可解释的成分的技术。它通过卷积稀疏编码方法,将图像表示为一组稀疏的、解耦的卷积核的组合。这种方法能够有效地提取图像中的关键特征,同时保持这些特征之间的独立性,有助于理解和分析图像内容。 3. ...
we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task. Specifically, we use multiple convolutional filters convolved on the sparse feature maps to deliver the former characteris...
The Convolutional Sparse Coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional... Ali Nozaripour,Hadi Soltanizadeh - The Visual Computer 被引量: 0发表: 2023年 A model-guided deep convolutional sparse coding network for...
This paper proposes a new visual tracking method by constructing the robust appearance model of the target with convolutional sparse coding. First, our method uses convolutional sparse coding to divide the interest region of the target into a smooth image and four detail images with different fitting...
Sparse coding models signal as a linear combinations of basis elements or atoms in a redundant dictionary. Dictionary design is, as much as possible, adapted to the self-feature of the data to well match the high-level structures embedded in the signal. CS has been widely used and has ...
A pytorch implementation of a ACSC model based onLerned Convolutional Sparse Codingmodel proposedhereand orhere. ACSC block description Evaluation of different ASCS variants ImageLenaHousePepperCoupleFgprBoatHillManBarbara ACSC131.2831.6929.8829.1126.4329.3129.3529.3627.85 ...
Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries ...
A sparse measurement matrix is proposed to extract thecompressed features for the appearance model without sacrificing the performance. We compress sample images of the foreground object and the background by the sparse matrix. When re-detection is needed, the tracking algorithm conducts an SVM ...
we address these issues and propose a novel probabilistic convolutional sparse coding (CSC) model for learning shift-invariant atoms from raw neural signals containing potentially severe artifacts. In the core of our model, which we callαCSC, lies a family of heavy-tailed distributions calledα-st...