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.

In the image fusion mission, the crucial task is to generate high-quality images for highlighting the key objects while enhancing the scenes to be understood. To complete this task and provide a powerful interpretability as well as a strong generalization ability in producing enjoyable fusion results which are comfortable for vision tasks (such as objects detection and their segmentation), we present a novel interpretable decomposition scheme and develop a target-aware Taylor expansion approximation (T ² EA) network for infrared and visible image fusion, where our T ² EA includes the following key procedures: Firstly, visible and infrared images are both decomposed into feature maps through a designed Taylor expansion approximation (TEA) network. Then, the Taylor feature maps are hierarchically fused by a dual-branch feature fusion (DBFF) network. Next, the fused map of each layer is contributed to synthesize an enjoyable fusion result by the inverse Taylor expansion. Finally, a segmentation network is jointed to refine the fusion network parameters which can promote the pleasing fusion results to be more suitable for segmenting the objects. To validate the effectiveness of our reported T ² EA network, we first discuss the selection of Taylor expansion layers and fusion strategies. Then, both quantitatively and qualitatively experimental results generated by the selected SOTA approaches on three datasets ( MSRS, TNO , and LLVIP ) are compared in testing, generalization, and target detection and segmentation, demonstrating that our T ² EA can produce more competitive fusion results for vision tasks and is more powerful for image adaption. The code will be available at https://github.com/MysterYxby/T ² EA.

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.