KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

Kim, Soo Ye; Sim, Hyeonjun; Kim, Munchurl

doi:10.1109/cvpr46437.2021.01047

Cited by 60 publications

(23 citation statements)

References 20 publications

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“…We compared our results with state-of-the-art methods, trained on a limited dataset of 36 training images and 4 validation images of size 640 × 640. The comparative results from Bicubic, SRResNet2 (with ZP), SRResNetp (ours, with PCP), EDSR [14], SRGAN [11], and KOALAnet [20] are summarized in Tab. 4 where SRResNetp has the same network structure with SRResNet2 but the ZP in SRResNet2 is replaced by PCP.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Residual Encoder Decoder Network (REDNet) [19] is a UNet like architecture with convolutional layer which extracts feature maps but preserves image structures, and deconvolutional layer learns to estimate the missing information of an LR input image. Kernel-Oriented Adaptive Local Adjustment (KOALAnet) [20] is a blind super resolution method, which aim to recover the HR image from a LR input image, degraded with unknown kernel. SRGAN is a GAN based network with SRResNet as generator and a simple CNN network as discriminator [11].…”

Section: Introductionmentioning

confidence: 99%

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SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Ullah¹,

Song²

2023

Computers, Materials &Amp; Continua

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Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively. One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections. However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases. In this paper. we consider the ResNet with Partial Convolution based Padding (PCP) instead of ZP to solve SR problem. Since training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities, patch image based SR performance is compared with single full image based one. The experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Ullah¹,

Song²

2023

Computers, Materials &Amp; Continua

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“…In [21], a mutual affine network (MANet) with a moderate receptive field is proposed to maintain the locality of degradation for spatially variant kernel estimation. Kim et al [23] use a downsampling network to estimate spatially variant kernels. The predicted kernels are then leveraged as local filtering operations for SR features modulation.…”

Section: Blind Super-resolutionmentioning

confidence: 99%

“…To overcome this issue, blind SR methods [15,16,17,18,19,20,21,22,23,24,25] propose to recover the HR image from an unknown degraded LR image. Some of them [19,20,21,22,24,25] divide this problem into degradation estimation and kernel-based SR reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

Bai

et al. 2024

IEEE Trans. Multimedia

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Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process (i.e. blur and downsampling) is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and realworld images show that the proposed method achieves the stateof-the-art performance compared to existing methods.

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“…To comprehensively test the performance of ESRGCNN, quantitative and qualitative analysis are chosen to conduct experiments in this paper. Specifically, the quantitative analysis uses SR results both of average PSNR and SSIM, running time of recovering high-quality image, model complexities and perceptual quality, i.e., feature similarity index (FSIM) [72] of popular SR methods, including Bicubic [54], A+ [61], RFL [50], self-exemplars super-resolution (SelfEx) [22], 30-layer residual encoder-decoder network (RED30) [46], the cascade of sparse coding based networks (CSCN) [64], trainable nonlinear reaction diffusion (TNRD) [5], a denoising convolutional neural network (DnCNN) [71], fast dilated super-resolution convolutional network (FDSR) [44], SRCNN [10], residue context network (RCN) [51], VDSR [29], context-wise network fusion (CNF) [49], Laplacian super-resolution network (LapSRN) [34], information distillation network (IDN) [25], DRRN [55], balanced two-stage residual networks (BTSRN) [13], MemNet [56], cascading residual network mobile (CARN-M) [2], end-to-end deep and shallow network (EEDS+) [62], deep recurrent fusion network (DRFN) [68], multiscale a dense lightweight network with L1 loss (MADNet-L 1 ) [35], multiscale a dense lightweight network with enhanced LF loss (MADNet-L F ) [35], multi-scale deep encoder-decoder with phase congruency (MSDEPC) [41], LESRCNN [60], DIP-FKP [38], DIP-FKP+USRNet [38], kernel-oriented adaptive local adjustment network (KOALAnet) [31], fast, accurate and lightweight super-resolution architectures and models (FALSR-C) [6], SRCondenseNet [28], structure-preserving super resolution method (SPSR) [45], residual dense network (RDN) ...…”

Section: Comparisons With State-of-the-artsmentioning

confidence: 99%

Image Super-resolution with An Enhanced Group Convolutional Neural Network

Tian¹,

Yuan²,

Zhang³

et al. 2022

Preprint

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CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image superresolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive 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. Code is found at https://github.com/hellloxiaotian/ESRGCNN.

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KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

Cited by 60 publications

References 20 publications

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

Image Super-resolution with An Enhanced Group Convolutional Neural Network

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