Existing deep image dehazing methods usually depend on supervised learning with a large number of hazy-clean image pairs which are expensive or difficult to collect. Moreover, dehazing performance of the learned model may deteriorate significantly when the training hazy-clean image pairs are insufficient and are different from real hazy images in applications. In this paper, we show that exploiting large scale training set and adapting to real hazy images are two critical issues in learning effective deep dehazing models. Under the depth guidance estimated by a well-trained depth estimation network, we leverage the conventional atmospheric scattering model to generate massive hazy-clean image pairs for the self-supervised pre-training of dehazing network. Furthermore, self-supervised adaptation is presented to adapt pre-trained network to real hazy images. Learning without forgetting strategy is also deployed in self-supervised adaptation by combining self-supervision and model adaptation via contrastive learning. Experiments show that our proposed method performs favorably against the state-of-the-art methods, and is quite efficient, i.e., handling a 4K image in 23 ms. The codes are available at https://github.com/DongLiangSXU/SLAdehazing.

In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase. Inspired by the dynamic deep networks that adapt the model structures or parameters conditioned on the inputs, we propose a DCP-guided hierarchical dynamic mechanism for image enhancement to adapt the model parameters and features from local to global as well as to keep spatial adjacency within the region. Specifically, channel-spatial-level, structure-level, and region-level dynamic components are sequentially applied. Channel-spatial-level dynamics obtain channel- and spatial-wise representation variations, and structure-level dynamics enable modeling geometric transformations and augment sampling locations for the varying local features to better describe the structures. In addition, a novel region-level dynamic is proposed to generate spatially continuous masks for dynamic features which capitalizes on the Dark Channel Priors (DCP). The proposed region-level dynamics benefit from exploiting the statistical differences between distorted and undistorted images. Moreover, the DCP-guided region generations are inherently spatial coherent which facilitates capturing local coherence of the images. The proposed method achieves state-of-the-art performance and generates visually pleasing images for multiple enhancement tasks,i.e. , image dehazing, image deraining and low-light image enhancement. The codes are available at https://github.com/DongLiangSXU/HDM.

The performance of person re-identification (Re-ID) has been seriously affected by the large cross-view appearance variations caused by mutual occlusions and background clutter. Hence, learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network’s attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches.

Learning the non-linear image upscaling process has previously been considered as a simple regression process, where various models have been utilized to describe the correlations between high-resolution (HR) and low-resolution (LR) images/patches. In this paper, we present a multitask learning framework based on deep neural network for image super-resolution, where we jointly consider the image super-resolution process and the image degeneration process. By sharing parameters between the two highly relevant tasks, the proposed framework could effectively improve the obtained neural network based mapping model between HR and LR image patches. Experimental results have demonstrated clear visual improvement and high computational efficiency, especially with large magnification factors.

Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former constraint reduces information loose by the negative coefficients and improves the coding stability, and the latter allows the sparseness to be self-adaptive to the local feature. The proposed coding scheme is then approximated by an neural network based encoder for speed-up. More importantly, the multi-layer neural network architecture allows us to apply a multi-task learning strategy to fuse information from multi-cue. Specifically, we take one type of descriptor, such as SIFT as the input, and enforce the learned encoder to produce sparse code that can reconstruct not only SIFT but also other types of descriptors such as color moments. In this way, we could achieve not only 10 to 33 times speed up for sparse-coding, the multi-cue enforced learning strategy gives the image feature extracted by MN3SE superior image classification accuracy.