Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2203.08921v1
• Date: Wed, 16 Mar 2022 20:10:41 GMT
• Title: Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
• Authors: Bin Sun, Yulun Zhang, Songyao Jiang, and Yun Fu
• Abstract summary: Convolutional neural network (CNN) has achieved great success on image super-resolution (SR) Most deep CNN-based SR models take massive computations to obtain high performance. We propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task.
• Score: 64.54162195322246
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Convolutional neural network (CNN) has achieved great success on image super-resolution (SR). However, most deep CNN-based SR models take massive computations to obtain high performance. Downsampling features for multi-resolution fusion is an efficient and effective way to improve the performance of visual recognition. Still, it is counter-intuitive in the SR task, which needs to project a low-resolution input to high-resolution. In this paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing an efficient and effective downsampling module into the SR task. The network contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample the input features and use grouped convolution to reduce the channels. Besides, we enhance the depthwise convolution's performance by adding the input feature to its output. Experiments on benchmark datasets show that our HPUN achieves and surpasses the state-of-the-art reconstruction performance with fewer parameters and computation costs.

Related papers

• HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution [5.110892180215454]
  lightweight methods for single image super-resolution achieved impressive performance due to limited hardware resources. We find that using residual connections after each block increases the model's storage and computational cost. We use depthwise separable convolutions, fully connected layers, and activation functions as the basic feature extraction modules.
  arXiv  Detail & Related papers  (2024-10-13T14:00:21Z)
• Efficient Model Agnostic Approach for Implicit Neural Representation Based Arbitrary-Scale Image Super-Resolution [5.704360536038803]
  Single image super-resolution (SISR) has experienced significant advancements, primarily driven by deep convolutional networks. Traditional networks are limited to upscaling images to a fixed scale, leading to the utilization of implicit neural functions for generating arbitrarily scaled images. We introduce a novel and efficient framework, the Mixture of Experts Implicit Super-Resolution (MoEISR), which enables super-resolution at arbitrary scales.
  arXiv  Detail & Related papers  (2023-11-20T05:34:36Z)
• WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution [2.0477182014909205]
  We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture. WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks.
  arXiv  Detail & Related papers  (2023-07-01T21:25:03Z)
• Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution [90.16462805389943]
  We develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Proposed method is $3times$ smaller than state-of-the-art efficient SR methods.
  arXiv  Detail & Related papers  (2023-02-27T14:19:31Z)
• Efficient Image Super-Resolution using Vast-Receptive-Field Attention [49.87316814164699]
  The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We propose VapSR, the VAst-receptive-field Pixel attention network.
  arXiv  Detail & Related papers  (2022-10-12T07:01:00Z)
• ShuffleMixer: An Efficient ConvNet for Image Super-Resolution [88.86376017828773]
  We propose ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Experimental results demonstrate that the proposed ShuffleMixer is about 6x smaller than the state-of-the-art methods in terms of model parameters and FLOPs.
  arXiv  Detail & Related papers  (2022-05-30T15:26:52Z)
• Image Super-resolution with An Enhanced Group Convolutional Neural Network [102.2483249598621]
  CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. We present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture. Experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR.
  arXiv  Detail & Related papers  (2022-05-29T00:34:25Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)

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.