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 evolution of the microstructure of a battery electrode is closely related to battery performance. Characterization and visualization of the evolution of the microstructure is essential for optimization of manufactured electrodes. The validity of the battery structure representation affects the accuracy of the extracted microstructure parameters. In this study, a mini-cylindrical battery is designed to allow microstructure parameters to be obtained at different states of charge, bearing in mind the influence of the real battery structure. An argon-ion cross-section polisher is used to obtain a large area of the electrode for observation. In addition, an image segmentation method based on a modified U-Net neural network is developed to enhance the quality of the extracted microstructure. The relationship between porosity and thickness at different states of electrode charge is presented through experiments and deep learning of images. This method provides new insight into the evolution of electrode microstructure and can potentially guide the manufacturing of lithium-ion batteries.

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.

The formulation of the hazy image is mainly dominated by the reflected lights and ambient airlight. Existing dehazing methods often ignore the depth cues and fail in distant areas where heavier haze disturbs the visibility. However, we note that the guidance of the depth information for transmission estimation could remedy the decreased visibility as distances increase. In turn, the good transmission estimation could facilitate the depth estimation for hazy images. In this paper, a deep end-to-end model that iteratively estimates image depths and transmission maps is proposed to perform an effective depth prediction for hazy images and improve the dehazing performance with the guidance of depth information. The image depth and transmission map are progressively refined to better restore the dehazed image. Our approach benefits from explicitly modeling the inner relationship of image depth and transmission map, which is especially effective for distant hazy areas. Extensive results on the benchmarks demonstrate that our proposed network performs favorably against the state-of-the-art dehazing methods in terms of depth estimation and haze removal.

The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when applied to real scenarios due to the unavailable or insufficient data problems. On the other hand, the physical model-based methods have better interpretability but suffer from multi-objective optimizations of parameters, which may lead to sub-optimal dehazing results. In this paper, a progressive residual learning strategy has been proposed to combine the physical model-free dehazing process with reformulated scattering model-based dehazing operations, which enjoys the merits of dehazing methods in both categories. Specifically, the global atmosphere light and transmission maps are interactively optimized with the aid of accurate residual information and preliminary dehazed restorations from the initial physical model-free dehazing process. The proposed method performs favorably against the state-of-the-art methods on public dehazing benchmarks with better model interpretability and adaptivity for complex hazy data.

The depth maps captured by sensors always suffer from low resolution and random noise. Recently, by introducing the guidance from the color image, deep convolutional neural network (DCNN) shows significant improvements for depth map enhancement. However, most DCNN-based methods do not make full use of multi-scale guidance from the color image, thereby achieving sub-optimal performances. In this paper, we propose a novel DCNN to progressively reconstruct the high-resolution depth map guided by the intensity image. Specially, the multi-scale intensity features are extracted to provide guidance for the refinement of depth features as their resolutions are gradually enhanced. Furthermore, local residual learning and global residual learning are adopted in the output of each up-sampling sub-network and the whole network respectively. Such design can recover the high-frequency details from coarse to fine. In addition, according to the contiguous memory mechanism, the dense connections are designed to take the low-level features and the high-level features into account which further exploits the guidance from the intensity image. To balance the resource expense and the performance, the dimension reduction units are used to efficiently represent the features. The proposed network is compared with 17 state-of-the-art methods which shows improved performances.