Real-world image dehazing is a challenging task due to the collection of aligned hazy/clear image pairs under unpredictable and complex environments. To address this limitation, we propose a Physical-Guided Posterior Sampling (PGPS) method that designs a dehazing reconstruction posterior to sample an RGB and depth from pre-trained unconditional diffusion generation process. First, we introduce a Hybrid Degradation Atmospheric Scattering Model (HD-ASM) to adapt the diffusion model, enabling the generation of high-fidelity dehazed images from posterior samples without relying on the aligned hazy/clear image pairs. Second, we propose a two-stage sampling strategy with piecewise loss to improve sampling quality and stability, along with a post-processing technique to remove JPEG compression artifacts amplified by dehazing. Extensive experiments show that our method outperforms state-of-the-art techniques in image dehazing, and in the RTTS dataset’s complex human-vehicle environment. Additionally, our approach also surpasses other benchmarks in object detection, exhibiting superior generalization performance.

Abstract Recent advances in deep convolutional neural networks have shown improved performance in face super‐resolution through joint training with other tasks such as face analysis and landmark prediction. However, these methods have certain limitations. One major limitation is the requirement for manual marking information on the dataset for multi‐task joint learning. This additional marking process increases the computational cost of the network model. Additionally, since prior information is often estimated from low‐quality faces, the obtained guidance information tends to be inaccurate. To address these challenges, a novel Decoder Structure Guided CNN‐Transformer Network (DCTNet) is introduced, which utilises the newly proposed Global‐Local Feature Extraction Unit (GLFEU) for effective embedding. Specifically, the proposed GLFEU is composed of an attention branch and a Transformer branch, to simultaneously restore global facial structure and local texture details. Additionally, a Multi‐Stage Feature Fusion Module is incorporated to fuse features from different network stages, further improving the quality of the restored face images. Compared with previous methods, DCTNet improves Peak Signal‐to‐Noise Ratio by 0.23 and 0.19 dB on the CelebA and Helen datasets, respectively. Experimental results demonstrate that the designed DCTNet offers a simple yet powerful solution to recover detailed facial structures from low‐quality images.

Super-resolution methods for facial image via representation learning scheme have become very effective methods due to their efficiency. The key problem for the super-resolution of facial image is to reveal the latent relationship between the low-resolution ( LR ) and the corresponding high-resolution ( HR ) training patch pairs. To simultaneously utilize the contextual information of the target position and the manifold structure of the primitive HR space, in this work, we design a robust context-patch facial image super-resolution scheme via a kernel locality-constrained coupled-layer regression (KLC2LR) scheme to obtain the desired HR version from the acquired LR image. Here, KLC2LR proposes to acquire contextual surrounding patches to represent the target patch and adds an HR layer constraint to compensate the detail information. Additionally, KLC2LR desires to acquire more high-frequency information by searching for nearest neighbors in the HR sample space. We also utilize kernel function to map features in original low-dimensional space into a high-dimensional one to obtain potential nonlinear characteristics. Our compared experiments in the noisy and noiseless cases have verified that our suggested methodology performs better than many existing predominant facial image super-resolution methods.

Deep stacking networks (DSNs) have been successfully applied in classification tasks. Its architecture builds upon blocks of simplified neural network modules (SNNM). The hidden units are assumed to be independent in the SNNM module. However, this assumption prevents SNNM from learning the local dependencies between hidden units to better capture the information in the input data for the classification task. In addition, the hidden representations of input data in each class can be expectantly split into a group in real-world classification applications. Therefore, we propose two kinds of group sparse SNNM modules by mixing -norm and -norm. The first module learns the local dependencies among hidden units by dividing them into non-overlapping groups. The second module splits the representations of samples in different classes into separate groups to cluster the samples in each class. A group sparse DSN (GS-DSN) is constructed by stacking the group sparse SNNM modules. Experimental results further verify that our GS-DSN model outperforms the relevant classification methods. Particularly, GS-DSN achieves the state-of-the-art performance (99.1%) on 15-Scene.

Sparse coding can learn good robust representation to noise and model more higher-order representation for image classification. However, the inference algorithm is computationally expensive even though the supervised signals are used to learn compact and discriminative dictionaries in sparse coding techniques. Luckily, a simplified neural network module (SNNM) has been proposed to directly learn the discriminative dictionaries for avoiding the expensive inference. But the SNNM module ignores the sparse representations. Therefore, we propose a sparse SNNM module by adding the mixed-norm regularization (l1/l2 norm). The sparse SNNM modules are further stacked to build a sparse deep stacking network (S-DSN). In the experiments, we evaluate S-DSN with four databases, including Extended YaleB, AR, 15 scene and Caltech101. Experimental results show that our model outperforms related classification methods with only a linear classifier. It is worth noting that we reach 98.8% recognition accuracy on 15 scene.

Low-rank representation (LRR) efficiently performs the subspace segmentation and feature extraction from corrupted data. However, there are three disadvantages in existing LRR techniques. First, the inference algorithm of LRR (as a generative model) is computationally expensive. Second, LRR ignores the discriminative information for image classification. Third, although the robust representation is implemented by recovering the low-rank components and the sparse noises, it has been limited due to the constrained assumption that noises is sparse. To solve these problems, and inspired by Denoising Autoencoders (DAE) and Contractive Autoencoders (CAE), this paper proposes a discriminative low-rank representations framework (DLRR) for image classification. We directly learn a discriminative projection dictionary that results in fast inference. Simultaneously, DLRR can obtain a robust representation from any corrupted input. Our implementation of DLRR achieves state-of-the-art results on artificial dataset and dataset of Olivetti Face Patches.