On robust estimation of the location parameter

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Remote Sensing And Image Interpretation

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.

Deep Learning for Image Super-Resolution: A Survey

As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectures. In this paper, we study the low-level computer vision task (e.g., denoising, super-resolution and deraining) and develop a new pre-trained model, namely, image processing transformer (IPT). To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs. The IPT model is trained on these images with multi-heads and multi-tails. In addition, the contrastive learning is introduced for well adapting to different image processing tasks. The pre-trained model can therefore efficiently employed on desired task after fine-tuning. With only one pre-trained model, IPT outperforms the current state-of-the-art methods on various low-level benchmarks. Code is available at this https URL and this https URL

Pre-Trained Image Processing Transformer

Recent studies have shown that deep neural networks can significantly improve the quality of single-image super-resolution. Current researches tend to use deeper convolutional neural networks to enhance performance. However, blindly increasing the depth of the network cannot ameliorate the network effectively. Worse still, with the depth of the network increases, more problems occurred in the training process and more training tricks are needed. In this paper, we propose a novel multi-scale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods. Based on the residual block, we introduce convolution kernels of different sizes to adaptively detect the image features in different scales. Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB). Furthermore, the outputs of each MSRB are used as the hierarchical features for global feature fusion. Finally, all these features are sent to the reconstruction module for recovering the high-quality image.

/pdf/multi-scale-residual-network-for-image-super-resolution-7tpo3b0eex.pdf

Multi-scale Residual Network for Image Super-Resolution

Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel is unknown. To solve this ill-posed problem, a great amount of image priors have been explored and employed in this area. In this paper, we present a blind deblurring method based on Local Maximum Gradient (LMG) prior. Our work is inspired by the simple and intuitive observation that the maximum value of a local patch gradient will diminish after the blur process, which is proved to be true both mathematically and empirically. This inherent property of blur process helps us to establish a new energy function. By introducing an liner operator to compute the Local Maximum Gradient, together with an effective optimization scheme, our method can handle various specific scenarios. Extensive experimental results illustrate that our method is able to achieve favorable performance against state-of-the-art algorithms on both synthetic and real-world images.

/pdf/blind-image-deblurring-with-local-maximum-gradient-prior-4g7s6c9p6u.pdf

Blind Image Deblurring With Local Maximum Gradient Prior

The task of single image super-resolution (SISR) is a highly ill-posed inverse problem since reconstructing the high-frequency details from a low-resolution image is challenging. Most previous CNN-based super-resolution (SR) methods tend to directly learn the mapping from the low-resolution image to the high-resolution image through some complex convolutional neural networks. However, the method of blindly increasing the depth of the network is not the best choice because the performance improvement of such methods is marginal but the computational cost is huge. A more efficient method is to integrate the image prior knowledge into the model to assist the image reconstruction. Indeed, the soft-edge has been widely applied in many computer vision tasks as the role of an important image feature. In this paper, we propose a Soft-edge assisted Network (SeaNet) to reconstruct the high-quality SR image with the help of image soft-edge. The proposed SeaNet consists of three sub-nets: a rough image reconstruction network (RIRN), a soft-edge reconstruction network (Edge-Net), and an image refinement network (IRN). The complete reconstruction process consists of two stages. In Stage-I, the rough SR feature maps and the SR soft-edge are reconstructed by the RIRN and Edge-Net, respectively. In Stage-II, the outputs of the previous stages are fused and then fed to the IRN for high-quality SR image reconstruction. Extensive experiments show that our SeaNet converges rapidly and achieves excellent performance under the assistance of image soft-edge. The code is available at https://gitlab.com/junchenglee/seanet-pytorch .

Soft-Edge Assisted Network for Single Image Super-Resolution

Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.

A Variational Approach for Pan-Sharpening

Pansharpening is a process of acquiring a multi-spectral image with high spatial resolution by fusing a low resolution multi-spectral image with a corresponding high resolution panchromatic image In this paper, a new pansharpening method based on the Bayesian theory is proposed The algorithm is mainly based on three assumptions: 1) the geometric information contained in the pan-sharpened image is coincident with that contained in the panchromatic image; 2) the pan-sharpened image and the original multi-spectral image should share the same spectral information; and 3) in each pan-sharpened image channel, the neighboring pixels not around the edges are similar We build our posterior probability model according to above-mentioned assumptions and solve it by the alternating direction method of multipliers The experiments at reduced and full resolution show that the proposed method outperforms the other state-of-the-art pansharpening methods Besides, we verify that the new algorithm is effective in preserving spectral and spatial information with high reliability Further experiments also show that the proposed method can be successfully extended to hyper-spectral image fusion

Faming Fang

Papers

Multi-scale Residual Network for Image Super-Resolution

Blind Image Deblurring With Local Maximum Gradient Prior

Soft-Edge Assisted Network for Single Image Super-Resolution

A Variational Approach for Pan-Sharpening

High-Quality Bayesian Pansharpening