Face detection from low-light images is challenging due to limited photons and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from multi-exposure , which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is face detection rather than image enhancement . Specifically, we propose a novel Recurrent Exposure Generation (REG) module and couple it seamlessly with a Multi-Exposure Detection (MED) module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named REGDet , is the first ‘detection-with-enhancement’ framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.

This paper proposes a deep learning method for low-light image enhancement, which exploits the generation capability of Neural Networks (NNs) while requiring no training samples except the input image itself. Based on the Retinex decomposition model, the reflectance and illumination of a low-light image are parameterized by two untrained NNs. The ambiguity between the two layers is resolved by the discrepancy between the two NNs in terms of architecture and capacity, while the complex noise with spatially-varying characteristics is handled by an illumination-adaptive self-supervised denoising module. The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment. Extensive experiments show that the proposed method not only outperforms existing non-learning-based and unsupervised-learning-based methods, but also competes favorably with some supervised-learning-based methods in extreme low-light conditions.

Images taken on snowy days often suffer from severe negative visual effects caused by snowflakes. The task of removing snowflakes from a snowy image is known as image desnowing, which is challenging as image details are easily mistakenly treated and thus may be significantly lost during snowflake removal. Leveraging invertible neural networks (INNs), this paper presents a deep learning-based method for single image desnowing, which can remove snowflakes accurately while preserving image details well. Interpreting desnowing as an image decomposition problem, we propose an INN composed of two asymmetric interactive paths for predicting a latent image and a snowflake layer respectively. Such an INN is able to progressively refine the features of both latent images and snowflake layers for disentanglement, while retaining all information possibly relevant to latent image reconstruction. In addition, an attentive coupling layer supervised by snowflake masks is introduced to enhance feature dismantlement and a coupling-in-coupling structure is developed for further improvement. Extensive experiments show that, the proposed method outperforms existing ones on three benchmark datasets of synthetic and real-world images, and meanwhile it also shows advantages in terms of model size and computational efficiency.

Image denoising is about removing measurement noise from input image for better signal-to-noise ratio. In recent years, there has been great progress on the development of data-driven approaches for image denoising, which introduce various techniques and paradigms from machine learning in the design of image denoisers. This paper aims at investigating the application of ensemble learning in image denoising, which combines a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble. Then, with an effective re-sampling scheme, several ensemble-learning-based image denoisers are constructed using different sequential combinations of multiple proposed base denoisers. The experiments showed that sequential ensemble learning can effectively boost the performance of image denoising.

In recent years, multi-view learning has emerged as a promising approach for 3D shape recognition, which identifies a 3D shape based on its 2D views taken from different viewpoints. Usually, the correspondences inside a view or across different views encode the spatial arrangement of object parts and the symmetry of the object, which provide useful geometric cues for recognition. However, such view correspondences have not been explicitly and fully exploited in existing work. In this paper, we propose a correspondence-aware representation (CAR) module, which explicitly finds potential intra-view correspondences and cross-view correspondences via $k$ NN search in semantic space and then aggregates the shape features from the correspondences via learned transforms. Particularly, the spatial relations of correspondences in terms of their viewpoint positions and intra-view locations are taken into account for learning correspondence-aware features. Incorporating the CAR module into a ResNet-18 backbone, we propose an effective deep model called CAR-Net for 3D shape classification and retrieval. Extensive experiments have demonstrated the effectiveness of the CAR module as well as the excellent performance of the CAR-Net.

Nonblind image deblurring is about recovering the latent clear image from a blurry one generated by a known blur kernel, which is an often-seen yet challenging inverse problem in imaging. Its key is how to robustly suppress noise magnification during the inversion process. Recent approaches made a breakthrough by exploiting convolutional neural network (CNN)-based denoising priors in the image domain or the gradient domain, which allows using a CNN for noise suppression. The performance of these approaches is highly dependent on the effectiveness of the denoising CNN in removing magnified noise whose distribution is unknown and varies at different iterations of the deblurring process for different images. In this article, we introduce a CNN-based image prior defined in the Gabor domain. The prior not only utilizes the optimal space-frequency resolution and strong orientation selectivity of the Gabor transform but also enables using complex-valued (CV) representations in intermediate processing for better denoising. A CV CNN is developed to exploit the benefits of the CV representations, with better generalization to handle unknown noises over the real-valued ones. Combining our Gabor-domain CV CNN-based prior with an unrolling scheme, we propose a deep-learning-based approach to nonblind image deblurring. Extensive experiments have demonstrated the superior performance of the proposed approach over the state-of-the-art ones.