Stripe and random noise are two different degradation phenomena that commonly coexist in optical remote sensing images, and they are often modeled as inverse problems. In model-based inverse problems, analysis and synthesis sparse representations (SSRs) are used as regularization terms to obtain approximate solutions due to their respective merits, i.e., the nonzero coefficients in SSR are usually used to describe an image, while the indexes of zeros in analysis sparse representation (ASR) are used to characterize the stripe. Inspired by these merits, we propose a unified variational framework, called a joint analysis and weighted synthesis (JAWS) sparsity model, to simultaneously separate the clean image and the stripe from a single optical remote sensing image. To solve the JAWS sparsity model efficiently, an alternating minimization optimization strategy is first employed to separate it into two subproblems that are used for different tasks. One called as weighted SSR (WSSR) is the main for optical remote sensing image denoising, which can be effectively solved by employing the weighted singular value thresholding operator, while the other called as ASR is the main approach for optical remote sensing image destriping, which is optimized by adopting the split Bregman iteration. By minimizing the two subproblems alternatively, the proposed JAWS sparsity model is efficiently solved. Finally, both quantitative and qualitative results of experiments on synthetic and real-world optical remote sensing images validate that the proposed approach is effective and even better than the state of the arts.

Enhancement and denoising have always been a pair of conflicting problems in image processing of computer vision. Inspired by an earlier dual-domain filter (DDF), this letter proposes a progressive DDF to simultaneously enhance and denoise low-quality optical remote-sensing images. The main procedure of the proposed enhancement filter has two parts. First, a bilateral filter is exploited as a guide filter to obtain high-contrast images, which are enhanced by a histogram modification method. Then, low-contrast useful structures are restored by a short-time Fourier transform and are enhanced using an adaptive correction parameter. Both the quantitative and qualitative results of experiments on synthetic and real-world low-quality remote-sensing images demonstrate that the proposed method performs well on contrast enhancement, structure preservation, and noise reduction. Moreover, its satisfactory computation time resulting from its simple implementation makes it suitable for extensive application.

Stripe and random noise are two different degradation phenomena commonly co-existing in optical remote sensing images, which are often modelled as inverse problems, respectively. When solving those inverse problems, model-based optimization and discriminative learning methods are fashionably employed but have their respective merits and drawbacks, e.g., model-based optimization methods are flexible but usually time-consuming while discriminative learning methods have fast testing speed but are limited by the specialized task. To improve testing speed and obtain good performance, this paper integrates deep convolutional neural network (DCNN) denoiser prior into unidirectional variation (UV) model, named as UV-DCNN, to simultaneously destripe and denoise optical remote sensing images. The proposed UV-DCNN method can be efficiently solved by the alternating minimization optimization method. Both quantitative and qualitative experiment results validate that the proposed method is effective and even better than the state-of-the-arts, its satisfactory computation time makes it suitable for extensive application.

The low storage and strong representation capabilities of hash codes for image retrieval has made hashing technologies very popular. Several existing deep hashing methods focus on the task of general image retrieval, while neglecting the task of fine-grained image retrieval. Recently, some fine-grained hashing methods have been proposed to capture the subtle differences, which mainly utilize the single-modality visual features to solve the discriminative region localization while ignoring the semantic information. In this paper, we propose a correlation filtering hashing (CFH) method to learn discrete binary codes, which can adequately take advantage of the cross-modal correlation between the semantic information and the visual features for discriminative region localization. Specifically, we utilize a feature pyramid network to learn multi-level visual features. Subsequently, the label vector is embedded into the visual space, which can be used as a correlation filter on the feature maps to capture the latent location of objects. Finally, we perform global average pooling over the output maps and concatenate the features of different levels to produce the hash codes of query images. Extensive experiments on two fine-grained datasets show that the proposed CFH outperforms the state-of-the-art hashing methods.

Gradient descent strategy, viewed as an important model optimization method, has been widely used for various tasks (such as model-based image denoising) of computer vision. In the gradient descent denoising model, the learning rate and residual component are two important parts to be adaptively estimated for its stable point. This letter proposes a deep gradient descent network (DGDNet), including two key points: one is that the learning rate is designed with eigenvalues of Hessian matrix of remotely sensed images (RSIs) and their local weighted factor, which can recognize structures from RSIs degraded by additive white Gaussian noise (AWGN). The other is that the residual part is calculated by an U-shaped network to speed up the DGDNet convergent to a fixed point. Finally, the two components are plugged into the gradient descent scheme and contribute to an enjoyable result with a few iterations. Quantitatively and qualitatively experimental results demonstrate that the proposed DGDNet can obtain a stable solution efficiently, and produce competitive denoising performance which is even better than that yielded by the state-of-the-art noise reduction methods.

Remotely sensed images degraded by additive white Gaussian noise (AWGN) are not beneficial for the analysis of their contents. Such a phenomenon is usually modeled as an inverse problem which can be solved by model-based optimization methods or discriminative learning approaches. The former pursue their pleasing performance at the cost of a highly computational burden while the latter are impressive for their fast testing speed but are limited by their application range. To join their merits, this letter proposes a nonlocal self-similar (NSS) block-based deep image denoising scheme, namely deep low-rank prior (DLRP), which includes the following key points: First, the low-rank property of the neighboring NSS patches ordered lexicographically is utilized to model a global objective function (GOF). Second, with the aid of an alternative iteration strategy, the GOF can be easily decomposed into two independent subproblems. One is a quadratic optimization problem, and has a closed-form solution. While the other is a low-rank minimization denoising problem and is learned by deep convolutional neural network (DCNN). Then, the deep denoiser, acted as a modular part, is plugged into the model-based optimization method with adaptive noise level estimation to solve the inverse problem. In the experiments, we first discuss parameter setting and the convergence. Then, quantitative/qualitative comparisons of experimental results validate that the DLRP is a flexible and powerful denoising method to achieve competitive performance which even outperforms those produced by state-of-the-arts.