Linear Array Network for Low-light Image Enhancement

• URL: http://arxiv.org/abs/2201.08996v1
• Date: Sat, 22 Jan 2022 08:44:02 GMT
• Title: Linear Array Network for Low-light Image Enhancement
• Authors: Keqi Wang and Ziteng Cui and Ge Wu and Yin Zhuang and Yuhua Qian
• Abstract summary: This paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. LASA is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters.
• Score: 11.84047819225589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship aggregation methods have been widely used in computer vision, but these methods are difficult to handle high-resolution images because of the high computational complexity. To solve this problem, this paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. Based on LASA, Linear Array Network (LAN) is proposed, which is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters. The code is released in \url{https://github.com/cuiziteng/LASA_enhancement}.

Related papers

• Breaking Complexity Barriers: High-Resolution Image Restoration with Rank Enhanced Linear Attention [54.42902794496325]
  Linear attention, a variant of softmax attention, demonstrates promise in global context modeling.<n>We propose Rank Enhanced Linear Attention (RELA), a simple yet effective method that enriches feature representations by integrating a lightweight depthwise convolution.<n>Building upon RELA, we propose an efficient and effective image restoration Transformer, named LAformer.
  arXiv  Detail & Related papers  (2025-05-22T02:57:23Z)
• C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales [6.700548615812325]
  We propose a novel framework, textbfC2D-ISR, for optimizing attention-based image super-resolution models. Our approach is based on a two-stage training methodology and a hierarchical encoding mechanism. In addition, we generalize the hierarchical encoding mechanism with existing attention-based network structures.
  arXiv  Detail & Related papers  (2025-03-17T21:52:18Z)
• VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis [8.10783983193165]
  Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are two dominant models for image analysis. This paper introduces VisionGRU, a novel RNN-based architecture designed for efficient image classification.
  arXiv  Detail & Related papers  (2024-12-24T05:27:11Z)
• Double-Shot 3D Shape Measurement with a Dual-Branch Network [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities. Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images. We show that our method can reduce fringe order ambiguity while producing high-accuracy results on a self-made dataset.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation [53.83313235792596]
  We present a new methodology for real-time semantic mapping from RGB-D sequences. It combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping. Our system achieves state-of-the-art semantic mapping quality within 2D-3D networks-based systems.
  arXiv  Detail & Related papers  (2023-06-28T22:36:44Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• LSR: A Light-Weight Super-Resolution Method [36.14816868964436]
  LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. It consists of three modules: 1) generation of a pool of rich and diversified representations in the neighborhood of a target pixel via unsupervised learning, 2) selecting a subset from the representation pool that is most relevant to the underlying super-resolution task automatically via supervised learning, 3) predicting the residual of the target pixel via regression.
  arXiv  Detail & Related papers  (2023-02-27T09:02:35Z)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• 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)
• Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning [73.75457731689858]
  We develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$2$F) for SISR. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods.
  arXiv  Detail & Related papers  (2020-11-13T06:01:46Z)
• PNEN: Pyramid Non-Local Enhanced Networks [23.17149002568982]
  We propose a novel non-local module, Pyramid Non-local Block, to build up connection between every pixel and all remain pixels. Based on the proposed module, we devise a Pyramid Non-local Enhanced Networks for edge-preserving image smoothing. We integrate it into two existing methods for image denoising and single image super-resolution, achieving consistently improved performance.
  arXiv  Detail & Related papers  (2020-08-22T03:10:48Z)

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.