Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs

• URL: http://arxiv.org/abs/2006.16644v1
• Date: Tue, 30 Jun 2020 10:12:37 GMT
• Title: Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
• Authors: Furkan Ozcelik, Ugur Alganci, Elif Sertel, Gozde Unal
• Abstract summary: CNN-based approaches have shown promising results in pansharpening of satellite images in recent years. We propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared to existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas CNN-based methods provide a reduced resolution panchromatic image as input to their model along with reduced resolution multispectral images, hence learn to increase their resolution together, we instead provide the grayscale transformed multispectral image as input, and train our model to learn the colorization of the grayscale input. We further address the fixed downscale ratio assumption during training, which does not generalize well to the full-resolution scenario. We introduce a noise injection into the training by randomly varying the downsampling ratios. Those two critical changes, along with the addition of adversarial training in the proposed PanColorization Generative Adversarial Networks (PanColorGAN) framework, help overcome the spatial detail loss and blur problems that are observed in CNN-based pansharpening. The proposed approach outperforms the previous CNN-based and traditional methods as demonstrated in our experiments.

Related papers

• Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation [4.246657212475299]
  We propose a new deep learning method for hyperspectral pansharpening. It inherits a simple single-band unsupervised pansharpening model nested in a sequential band-wise adaptive scheme. The proposed method achieves very good results on our datasets, outperforming both traditional and deep learning reference methods.
  arXiv  Detail & Related papers  (2023-11-11T08:53:54Z)
• GRIG: Few-Shot Generative Residual Image Inpainting [27.252855062283825]
  We present a novel few-shot generative residual image inpainting method that produces high-quality inpainting results. The core idea is to propose an iterative residual reasoning method that incorporates Convolutional Neural Networks (CNNs) for feature extraction. We also propose a novel forgery-patch adversarial training strategy to create faithful textures and detailed appearances.
  arXiv  Detail & Related papers  (2023-04-24T12:19:06Z)
• Restormer: Efficient Transformer for High-Resolution Image Restoration [118.9617735769827]
  convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data. Transformers have shown significant performance gains on natural language and high-level vision tasks. Our model, named Restoration Transformer (Restormer), achieves state-of-the-art results on several image restoration tasks.
  arXiv  Detail & Related papers  (2021-11-18T18:59:10Z)
• Deep Gradient Projection Networks for Pan-sharpening [20.929492740317915]
  This paper develops a model-based deep pan-sharpening approach. By stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.
  arXiv  Detail & Related papers  (2021-03-08T07:51:58Z)
• Image Restoration by Deep Projected GSURE [115.57142046076164]
  Ill-posed inverse problems appear in many image processing applications, such as deblurring and super-resolution. We propose a new image restoration framework that is based on minimizing a loss function that includes a "projected-version" of the Generalized SteinUnbiased Risk Estimator (GSURE) and parameterization of the latent image by a CNN.
  arXiv  Detail & Related papers  (2021-02-04T08:52:46Z)
• Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising [104.59305271099967]
  We present a pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. We develop a pixel aggregation network for video denoising to sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes.
  arXiv  Detail & Related papers  (2021-01-26T13:00:46Z)
• Encoding Robustness to Image Style via Adversarial Feature Perturbations [72.81911076841408]
  We adapt adversarial training by directly perturbing feature statistics, rather than image pixels, to produce robust models. Our proposed method, Adversarial Batch Normalization (AdvBN), is a single network layer that generates worst-case feature perturbations during training.
  arXiv  Detail & Related papers  (2020-09-18T17:52:34Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• Super-resolution of multispectral satellite images using convolutional neural networks [0.0]
  We propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles. The derived quality metrics show that the method improves information content of the processed images.
  arXiv  Detail & Related papers  (2020-02-03T07:06:36Z)
• Supervised and Unsupervised Learning of Parameterized Color Enhancement [112.88623543850224]
  We tackle the problem of color enhancement as an image translation task using both supervised and unsupervised learning. We achieve state-of-the-art results compared to both supervised (paired data) and unsupervised (unpaired data) image enhancement methods on the MIT-Adobe FiveK benchmark. We show the generalization capability of our method, by applying it on photos from the early 20th century and to dark video frames.
  arXiv  Detail & Related papers  (2019-12-30T13:57:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.