Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement (LLIE). However, the hand-crafted priors and conventional optimization algorithm adopted to solve the layer decomposition problem result in the lack of adaptivity and efficiency. To this end, this paper proposes a Retinex-based deep unfolding network (URetinex-Net++), which unfolds an optimization problem into a learnable network to decompose a low-light image into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are carefully designed, responsible for data-dependent initialization, high-efficient unfolding optimization, and fairly-flexible component adjustment, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in the data-driven manner, can realize noise suppression and details preservation for decomposed components. URetinex-Net++ is a further augmented version of URetinex-Net, which introduces a cross-stage fusion block to alleviate the color defect in URetinex-Net. Therefore, boosted performance on LLIE can be obtained in both visual quality and quantitative metrics, where only a few parameters are introduced and little time is cost. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed URetinex-Net++ over state-of-the-art methods.

In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model by means of elegantly modeling the label information. The proposed model is capable of generating discriminable low-dimensional representations to improve clustering performance. Specifically, a pair of complementary regularizers, i.e., similarity and dissimilarity regularizers, is incorporated into the conventional NMF to guide the factorization. And, they impose restrictions on both the similarity and dissimilarity of the low-dimensional representations of data samples with labels as well as a small number of unlabeled ones. The proposed model is formulated as a well-posed constrained optimization problem and further solved with an efficient alternating iterative algorithm. Moreover, we theoretically prove that the proposed algorithm can converge to a limiting point that meets the Karush-Kuhn-Tucker conditions. Extensive experiments as well as comprehensive analysis demonstrate that the proposed model outperforms the state-of-the-art NMF methods to a large extent over five benchmark data sets, i.e., the clustering accuracy increases to 82.2% from 57.0%.

Utilizing data acquired by multiple wearable sensors can usually guarantee more accurate recognition for deep learning based human activity recognition. However, an increased number of sensors bring high processing cost, influencing real-time activity monitoring. Besides, existing methods rarely consider the interpretability of the recognition model in aspects of both the importance of the sensors and features, causing a gap between deep learning and their extendability in real-world scenario. In this paper, we cast the classical fused lasso model into a deep neural network, proposing a deep fused Lasso net (dfLasso-Net), which can perform sensor selection, feature selection and HAR in one end-to-end structure. Specifically, a two-level weight computing module (TLWCM) consisting of a senor weight net and a feature weight net is designed to measure the importance of sensors and features. In sensor weight net, spatial smoothness between physical channels within each sensor is considered to maximize the usage of selected sensors. And the feature weight net is able to maintain the physical meaning of the hand-crafted features through feature selection inside the sensors. By combining with the learning module for classification, HAR can be performed. We test dfLasso-Net on three multi-sensor based HAR datasets, demonstrating that dfLasso-Net achieves better recognition accuracy with the least number of sensors and provides good model interpretability by visualizing the weights of the sensors and features. Last but not least, dflasso-Net can be used as an effective filter-based feature selection approach with much flexibility.

Nonnegative matrix factorization (NMF) is a well-known paradigm for data representation. Traditional NMF-based classification methods first perform NMF or one of its variants on input data samples to obtain their low-dimensional representations, which are successively classified by means of a typical classifier (e.g., k-nearest neighbors (KNN) and support vector machine (SVM)). Such a stepwise manner may overlook the dependency between the two processes, resulting in the compromise of the classification accuracy. In this paper, we elegantly unify the two processes by formulating a novel constrained optimization model, namely dual embedding regularized NMF (DENMF), which is semi-supervised. Our DENMF solution simultaneously finds the low-dimensional representations and assignment matrix via joint optimization for better classification. Specifically, input data samples are projected onto a couple of low-dimensional spaces (i.e., feature and label spaces), and locally linear embedding is employed to preserve the identical local geometric structure in different spaces. Moreover, we propose an alternating iteration algorithm to solve the resulting DENMF, whose convergence is theoretically proven. Experimental results over five benchmark datasets demonstrate that DENMF can achieve higher classification accuracy than state-of-the-art algorithms.

Through formulating the image restoration as a generation problem, the conditional diffusion model has been applied to low-light image enhancement (LIE) to restore the details in dark regions. However, in the previous diffusion model based LIE methods, the conditions used for guiding generation are degraded images, such as low-light image, signal-to-noise ratio map and color map, which suffer from severe degradation and are simply fed into diffusion model by rigidly concatenating with the noise. To avoid using degraded conditions resulting in sub-optimal performance in recovering details and enhancing brightness, we use the image intrinsic components originating from the Retinex model as guidance, whose multi-scale features are flexibly integrated into the diffusion model, and propose a novel conditional diffusion model for LIE. Specifically, the input low-light image is decomposed into reflectance and illumination by a Retinex decomposition module, where two components contain abundant physical property and lighting conditions of the scene. Then, we extract the latent features from two conditions through a component-dependent feature extraction module, which is designed according to the physical property of components. Finally, instead of previous rigid concatenation manner, a well-designed feature fusion mechanism is equipped to adaptively embed generative conditions into diffusion model. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods, and is capable of effectively restoring the local details while brightening the dark regions.