Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission

• URL: http://arxiv.org/abs/2009.04127v2
• Date: Tue, 22 Sep 2020 06:27:21 GMT
• Title: Single Image Super-Resolution for Domain-Specific Ultra-Low Bandwidth Image Transmission
• Authors: Jesper Haahr Christensen, Lars Valdemar Mogensen, Ole Ravn
• Abstract summary: Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30--50 kbit/s. This is investigated on a large, diverse dataset obtained during years of trawl fishing where cameras have been placed in the fishing nets. A neural network is then trained to perform up-sampling, trying to reconstruct the original image.
• Score: 1.5469452301122177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-bandwidth communication, such as underwater acoustic communication, is limited by best-case data rates of 30--50 kbit/s. This renders such channels unusable or inefficient at best for single image, video, or other bandwidth-demanding sensor-data transmission. To combat data-transmission bottlenecks, we consider practical use-cases within the maritime domain and investigate the prospect of Single Image Super-Resolution methodologies. This is investigated on a large, diverse dataset obtained during years of trawl fishing where cameras have been placed in the fishing nets. We propose down-sampling images to a low-resolution low-size version of about 1 kB that satisfies underwater acoustic bandwidth requirements for even several frames per second. A neural network is then trained to perform up-sampling, trying to reconstruct the original image. We aim to investigate the quality of reconstructed images and prospects for such methods in practical use-cases in general. Our focus in this work is solely on learning to reconstruct the high-resolution images on "real-world" data. We show that our method achieves better perceptual quality and superior reconstruction than generic bicubic up-sampling and motivates further work in this area for underwater applications.

Related papers

• Capsule Enhanced Variational AutoEncoder for Underwater Image Reconstruction [8.16306466526838]
  We introduce a novel architecture that jointly tackles both issues by drawing inspiration from the discrete features quantization approach of Vector Quantized Variational Autoencoder (myVQVAE) Our model combines an encoding network, that compresses the input into a latent representation, with two independent decoding networks, that enhance/reconstruct images using only the latent representation. With the usage of capsule layers, we also overcome the differentiabilty issues of myVQVAE making our solution trainable in an end-to-end fashion without the need for particular optimization tricks.
  arXiv  Detail & Related papers  (2024-06-03T13:04:42Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring [23.86692375792203]
  Image deblurring is a computer vision problem that aims to recover a sharp image from a blurred image. Our model uses dilated convolution to enable the obtainment of the large receptive field with high spatial resolution. We propose a novel module using the wavelet transform, which effectively helps the network to recover clear high-frequency texture details.
  arXiv  Detail & Related papers  (2021-10-12T07:58:10Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Exploiting Raw Images for Real-Scene Super-Resolution [105.18021110372133]
  We study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. We also develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images.
  arXiv  Detail & Related papers  (2021-02-02T16:10:15Z)
• High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network [21.977093907114217]
  Single image super-resolution is crucial for many applications such as target detection and image classification. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images.
  arXiv  Detail & Related papers  (2020-10-01T15:06:02Z)
• Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting [12.839962012888199]
  We propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents. CRA mechanism produces high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches. We train the proposed model on small images with resolutions 512x512 and perform inference on high-resolution images, achieving compelling inpainting quality.
  arXiv  Detail & Related papers  (2020-05-19T18:55:32Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.