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Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

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Pansharpening algorithms are designed to enhance the spatial resolution of multispectral images using panchromatic images with high spatial resolutions. Panchromatic and multispectral images acquired from very high resolution (VHR) satellite sensors used as input data in the pansharpening process are characterized by spatial dissimilarities due to differences in their spectral/spatial characteristics and time lags between panchromatic and multispectral sensors. In this manuscript, a new pansharpening framework is proposed to improve the spatial clarity of VHR satellite imagery. This algorithm aims to remove the spatial dissimilarity between panchromatic and multispectral images using guided filtering (GF) and to generate the optimal local injection gains for pansharpening. First, we generate optimal multispectral images with spatial characteristics similar to those of panchromatic images using GF. Then, multiresolution analysis (MRA)-based pansharpening is applied using normalized difference vegetation index (NDVI)-based optimal injection gains and spatial details obtained through GF. The algorithm is applied to Korea multipurpose satellite (KOMPSAT)-3/3A satellite sensor data, and the experimental results show that the pansharpened images obtained with the proposed algorithm exhibit a superior spatial quality and preserve spectral information better than those based on existing algorithms.

Recent advances in deep learning have shown impressive performances for pan-sharpening. Pan-sharpening is the task of enhancing the spatial resolution of a multi-spectral (MS) image by exploiting the high-frequency information of its corresponding panchromatic (PAN) image. Many deep-learning-based pan-sharpening methods have been developed recently, surpassing the performances of traditional pansharpening approaches. However, most of them are trained in lower scales using misaligned PAN-MS training pairs, which has led to undesired artifacts and unsatisfying visual quality. In this paper, we propose an unsupervised learning framework with registration learning for pan-sharpening, called UPSNet. UPSNet can be effectively trained in the original scales, and implicitly learns the registration between PAN and MS images without any dedicatedly designed registration module involved. Additionally, we design two novel loss functions for training UPSNet: a guided-filter-based color loss between network outputs and aligned MS targets; and a dual-gradient detail loss between network outputs and PAN inputs. Extensive experimental results show that our UPSNet can generate pan-sharpened images with remarkable improvements in terms of visual quality and registration, compared to the state-of-the-art methods.

This paper presents an image deblurring method using 0-norm based deblurring and 2-norm based textureaware image fusion for remote sensing images. To restore the details of blurred texture, the proposed method first perform texture restoration by fusing the restored results using Richardson-Lucy deconvolution and unsharp masking. Next, we analyzed the intensity and dark channel properties of remote sensing images and perform the 0-norm based deblurring using the intensity and dark channel priors. Although the 0-norm based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed 2norm based image fusion method can preserve both sharp edges and texture details. In the experiments, we demonstrate that the proposed method can provide better restored results than existing state-of-the-art deblurring methods without over-smoothing and undesired artifact.

Image registration is an important task in many computer vision applications such as fusion systems, 3D shape recovery and earth observation. Particularly, registering satellite images is challenging and time-consuming due to limited resources and large image size. In such scenario, state-of-the-art image registration methods such as scale-invariant feature transform (SIFT) may not be suitable due to high processing time. In this paper, we propose an algorithm based on block processing via entropy to register satellite images. The performance of the proposed method is evaluated using different real images. The comparative analysis shows that it not only reduces the processing time but also enhances the accuracy.