Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution

• URL: http://arxiv.org/abs/2405.07023v1
• Date: Sat, 11 May 2024 14:21:40 GMT
• Title: Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution
• Authors: Long Peng, Yang Cao, Renjing Pei, Wenbo Li, Jiaming Guo, Xueyang Fu, Yang Wang, Zheng-Jun Zha,
• Abstract summary: We introduce kernel-wise differential operations within the convolutional kernel and develop several learnable directional gradient convolutions. These convolutions are integrated in parallel with a novel linear weighting mechanism to form an Adaptive Directional Gradient Convolution (DGConv) We further devise an Adaptive Information Interaction Block (AIIBlock) to adeptly balance the enhancement of texture and contrast while meticulously investigating the interdependencies, culminating in the creation of a DGPNet for Real-SR through simple stacking.
• Score: 80.85121353651554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-SR endeavors to produce high-resolution images with rich details while mitigating the impact of multiple degradation factors. Although existing methods have achieved impressive achievements in detail recovery, they still fall short when addressing regions with complex gradient arrangements due to the intensity-based linear weighting feature extraction manner. Moreover, the stochastic artifacts introduced by degradation cues during the imaging process in real LR increase the disorder of the overall image details, further complicating the perception of intrinsic gradient arrangement. To address these challenges, we innovatively introduce kernel-wise differential operations within the convolutional kernel and develop several learnable directional gradient convolutions. These convolutions are integrated in parallel with a novel linear weighting mechanism to form an Adaptive Directional Gradient Convolution (DGConv), which adaptively weights and fuses the basic directional gradients to improve the gradient arrangement perception capability for both regular and irregular textures. Coupled with DGConv, we further devise a novel equivalent parameter fusion method for DGConv that maintains its rich representational capabilities while keeping computational costs consistent with a single Vanilla Convolution (VConv), enabling DGConv to improve the performance of existing super-resolution networks without incurring additional computational expenses. To better leverage the superiority of DGConv, we further develop an Adaptive Information Interaction Block (AIIBlock) to adeptly balance the enhancement of texture and contrast while meticulously investigating the interdependencies, culminating in the creation of a DGPNet for Real-SR through simple stacking. Comparative results with 15 SOTA methods across three public datasets underscore the effectiveness and efficiency of our proposed approach.

Related papers

• Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration [59.744840744491945]
  We reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency. We propose cost-aware trajectory distillation to streamline complex paths into several manageable steps with adaptable sizes. Experiments showcase the significant superiority of the proposed method, achieving a maximum PSNR improvement of 2.1 dB over state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-07T07:46:08Z)
• Implicit Gaussian Splatting with Efficient Multi-Level Tri-Plane Representation [45.582869951581785]
  Implicit Gaussian Splatting (IGS) is an innovative hybrid model that integrates explicit point clouds with implicit feature embeddings. We introduce a level-based progressive training scheme, which incorporates explicit spatial regularization. Our algorithm can deliver high-quality rendering using only a few MBs, effectively balancing storage efficiency and rendering fidelity.
  arXiv  Detail & Related papers  (2024-08-19T14:34:17Z)
• Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution [33.16889233975723]
  Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community. We propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework.
  arXiv  Detail & Related papers  (2024-08-10T04:51:43Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• Large-scale Global Low-rank Optimization for Computational Compressed Imaging [8.594666859332124]
  We present the global low-rank (GLR) optimization technique, realizing highly-efficient large-scale reconstruction with global self-similarity. Inspired by the self-attention mechanism in deep learning, GLR extracts image patches by feature detection instead of conventional uniform selection. We experimentally demonstrate GLR's effectiveness on temporal, frequency, and spectral dimensions.
  arXiv  Detail & Related papers  (2023-01-08T14:12:51Z)
• Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation [5.238765582868391]
  We propose a learned multi-resolution image compression framework, which exploits octave convolutions to factorize the latent representations into the high-resolution (HR) and low-resolution (LR) parts. Experimental results show that our method separately reduces the decoding time by approximately 73.35 % and 93.44 % compared with that of state-of-the-art learned image compression methods.
  arXiv  Detail & Related papers  (2022-09-07T08:21:52Z)
• Universal Generative Modeling for Calibration-free Parallel Mr Imaging [13.875986147033002]
  We present an unsupervised deep learning framework for calibration-free parallel MRI. We make use of the merits of both wavelet transform and the adaptive iteration strategy in a unified framework. We train a powerful noise conditional score network by forming wavelet tensor as the network input.
  arXiv  Detail & Related papers  (2022-01-25T10:05:39Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• Cogradient Descent for Dependable Learning [64.02052988844301]
  We propose a dependable learning based on Cogradient Descent (CoGD) algorithm to address the bilinear optimization problem. CoGD is introduced to solve bilinear problems when one variable is with sparsity constraint. It can also be used to decompose the association of features and weights, which further generalizes our method to better train convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2021-06-20T04:28:20Z)
• Cogradient Descent for Bilinear Optimization [124.45816011848096]
  We introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent. Our algorithm is applied to solve problems with one variable under the sparsity constraint.
  arXiv  Detail & Related papers  (2020-06-16T13:41:54Z)

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.