Image Restoration Using Deep Regulated Convolutional Networks

Liu, Peng; Zhou, Xiaoxiao; Yang, Junyi; Basha, Mohammad D. El; Fang, Ruogu

doi:10.48550/arxiv.1910.08853

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Zhang et al [34] worked on Ultra High Definition (UHD), introducing two datasets UHDSR4K and UHDSR8K which remain limited to this type of image. Liu et al [35] [36] MFSRCNN [54] BTSRN [70] BSRN [83] 4PP-EUSR [93] ABPN [102] FAFR [100] CSNLN [37] CARN [71] DBPN-RES-MR64-3 [84] Edge-informed SR [94] DRLN [72] JMPF+ [93] CSRCNN [38] MDSR [55] CARN-M [71] D-DBPN [84] HiFaceGAN [13] HAN+ [103] CMSC [39] MWCNN [56] DRLN+ [72] DRCN [72] Nearest neighbors [77] SelNet [104] DnCNN [40] N3Net [57] EDSR [73] DRRN [85] ESRGAN [82] SRRAM [105] FALSR-A [41] DnCNN-3 [40] IKC [74] HBPN [86] ProSR [95] PASSRnet [106] FSRCNN [42] Perceptual Loss [58] REDNet [75] DSRN [87] RFN [96] SAN [107] CNF [43] ESPCN [59] LCSCNet [76] LapSRN <...…”

Section: Hr=sr(lrf)mentioning

confidence: 99%

Study of Deep Learning-based models for Single Image Super-Resolution

Soufi¹,

Belouadha²

2022

RIA

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The super-resolution of images has seen remarkable progress, especially with the use of deep learning models. This technique allows having a better-quality image from one or more low-resolution versions. Super-resolution, therefore, aims at enriching a lowresolution image with additional pixel density and high-frequency detail. This paper presents a comprehensive empirical study based on a systematic review of deep learningbased models for single image super-resolution (SISR), exploring the set of techniques offered by deep learning technology and used for SISR. In this paper, we present a global and complete state of the art on deep learning model based on reference metrics (mainly Peak Signal to Noise Ratio -PSNR-and Structural SIMilarity -SSIM-) in the field of computer visualization and image reconstruction. This study was done on several deep learning designs with 90 different models tested on 7 reference datasets in the computer vision domain. Thus, our goal is to present a benchmark to demonstrate the performance and limitations of these models as well as to guide future research in the field of super image resolution to develop efficient algorithms. Moreover, our study covers different neural network architectures (Generative Adversarial Networks -GAN-, Convolutional Neural Networks -CNN-, and Recurrent Neural Networks -RNN-...), using different techniques and technologies.

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