A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution

By Tomer Peleg, Michael Elad. 2014

401 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, methodology): “…fac-( tor of 4; 3) type III: anisotropic Gaussian blur with nearest downsampling by a scale factor of 2 and subsequent bicubic downsampling by another scale factor of 2 and ﬁ-nal JPEG compression with quality factors uniformly sampled from [41 , 90] ; and 4) type IV: our proposed degradation model.”
Image Super-Resolution Using Very Deep Residual Channel Attention Networks (2018, methodology): “Urban100 (3×): img 062 HR Bicubic SPMSR [44] SRCNN [1] FSRCNN [2] PSNR/SSIM 20.”
Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications (2016, methodology): “[17] A. Rond, R. Giryes, and M. Elad, “Poisson inverse problems by the plugand-play scheme,” Available online: http://arxiv.org/abs/1511.02500, Nov 2015.”
Feedback Network for Image Super-Resolution (2019, methodology): “To address this problem, numerous image SR methods have been proposed, including interpolation-based methods[45], reconstruction-based methods[42], and learning-based methods [33, 26, 34, 15, 29, 6, 18].”
Ultra-Resolving Face Images by Discriminative Generative Networks (2016, methodology): “Generic super-resolution: In general, generic single image super-resolution methods have three types: interpolation based methods, image statistics based methods [20,21] and example (patch)-based methods [22,23,24,7,25,26].”
Residual Feature Aggregation Network for Image Super-Resolution (2020, methodology): “We compare our RFANet with 10 state-of-the-art methods: SPMSR [22], SRCNN [4], FSRCNN [5], VDSR [13], IRCNN [35], SRMD [36], RDN [40], SRFBN [17], RCAN [38], and SAN [ 3].”
Second-Order Attention Network for Single Image Super-Resolution (2019, methodology): “We compare our method with 8 state-of-the-art SR methods: SPMSR [24], SRCNN [2], FSRCNN [3], VDSR [12], IR-CNN [35], SRMD [36], RDN [39], and RCAN [38].”
Residual Dense Network for Image Restoration (2018, background): “Recently, deep convolutional neural network (CNN) has shown its excellent performance in different image restoration tasks, such as image super-resolution (SR) [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], image denoising (DN) [15], [16], [17], [18], [19], [20], and image compression r”
Densely Residual Laplacian Super-Resolution (2019, methodology): “We present the comparison of our model with the state-of-the-art CNN models which include SRCNN [28], FSRCNN [20], VDSR [9], SCN [52], SPMSR [53], LapSRN [10], MSLap-SRN [6], MemNet [23], EDSR [5], SRMDNF [22], D-DBPN [18], IRCNN [21], RDN [15], RCAN [7] and CARN [8].”
Deep Unfolding Network for Image Super-Resolution (2020, methodology): “In [15, 42], the degradation model focuses on Gaussian blurring and a subsequent downsampling with scale factor 3.”
Residual Dense Network for Image Super-Resolution (2018, methodology): “Our RDN is compared with SPMSR [19], SRCNN [3], FSRCNN [4], VDSR [10], IRCNN G [35], and IRCNN C [35].”
Seven Ways to Improve Example-Based Single Image Super Resolution (2015, methodology): “The method of Peleg and Elad [23] and the SRCNN method of Dong et al .”
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (2017, background): “Existing literatures have considered two types of downsamplers, including direct downsampler [11, 17, 36, 51, 55] and bicubic downsampler [7, 12, 14, 15, 47, 52].”
Learning Deep CNN Denoiser Prior for Image Restoration (2017, methodology): “, SRCNN [21] and VDSR [35]), one statistical prediction model based discriminative learning method [45] which we refer to as SPMSR, one model based optimization method (i.”
Image super-resolution: The techniques, applications, and future (2016, methodology): “The main advances since then include different training approaches for the dictionary pair [159,166,167], efficiency improvements [168], and various coding strategies [164,169].”
Convolutional Sparse Coding for Image Super-Resolution (2015, methodology): “Peleg and Elad [17] proposed a statistical model which uses restricted Boltzmann machine (RBM) to model the relationship between the LR and HR coding vectors.”
Correction to: Single Image Super-Resolution via a Holistic Attention Network (2020, methodology): “We compare the proposed method with nine state-of-the-art super-resolution methods: SPMSR [25], SRCNN [3], FSRCNN [4], VDSR [15], IRCNN [37], SRMD [39], RDN [41], RCAN [40],SRFBN [21] and SAN [2].”