First-order derivative-based super-resolution

Haris, Muhammad; Widyanto, M. Rahmat; Nobuhara, Hajime

doi:10.1007/s11760-016-0880-y

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Back-projection [23] is an efficient iterative procedure to minimize the reconstruction error. Previous studies have proven the effectiveness of back-projection [41], [42], [43], [44]. Originally, back-projection in SR was designed for the case with multiple LR inputs.…”

Section: Back-projectionmentioning

confidence: 99%

Deep Back-Projection Networks for Super-Resolution

Haris

Shakhnarovich

Ukita

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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1,322

940

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Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low-and high-resolution images. We propose Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges (NTIRE2018 and PIRM2018), that exploit iterative up-and down-sampling layers. These layers are formed as a unit providing an error feedback mechanism for projection errors. We construct mutually-connected up-and down-sampling units each of which represents different types of image degradation and high-resolution components. We also show that extending this idea to several variants applying the latest deep network trends, such as recurrent network, dense connection, and residual learning, to improve the performance. The experimental results yield superior results and in particular establishing new state-of-the-art results across multiple data sets, especially for large scaling factors such as 8×.

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Section: Back-projectionmentioning

confidence: 99%

Deep Back-Projection Networks for Super-Resolution

Haris

Shakhnarovich

Ukita

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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1,322

940

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“…where F represents the blurring filter of the imaging system, and the filter estimated by the blind deblurring method is used here, and ↓ r denotes downsampling operator with scale factor r (r = 4 is usually found in pansharpening). The IBP scheme [35] is a common method for solving the above model and has been successfully applied in superresolution literature [36], [37]. It was first proposed in [38] for pansharpening postprocessing.…”

Section: Spatial Enhancementmentioning

confidence: 99%

Image Restoration for the MRA-Based Pansharpening Method

Jiao

2020

IEEE Access

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By merging high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images, high-resolution MS images with complementary information can be obtained, i.e., pansharpening. Multiresolution analysis (MRA) methods have attracted widespread attention in the pansharpening field. The spatial detail information injected into MS images is extracted from PAN images by MRA tools. Since such methods often suffer from spatial distortion and ringing artifacts, a restoration algorithm based on blind deblurring and iterative back-projection (IBP) is proposed in this paper. First, a blind deblurring method based on the Tikhonov regular constraint model is used to estimate the blurring filter. Second, spatial details extracted from PAN images are modulated into MS images using a high-pass modulation (HPM) framework, and then fusion images are spatially enhanced based on the modulation results and blurring filter. Finally, the IBP technique is used to project the reconstruction error back to iteratively update and optimize the desired high-resolution images. Experiments are performed on data sets acquired by different satellites at full and reduced resolution, and eight state-of-the-art MRA-based pansharpening methods are used for validation. Compared to the enhanced back-projection (EBP) algorithm, the proposed restoration method is better in improving spectral and spatial quality of MRA-based pansharpening. The results indicate the effectiveness and superiority of the proposed method. INDEX TERMS Blind deblurring, image restoration, iterative back-projection, multiresolution analysis, pansharpening.

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“…Based on the above image acquisition model, back-projection (BP) was firstly proposed by Iranni and Peleg in [50,57] to reconstruct the ideal high resolution image x. It is a popular method and applied successfully in the field of image super-resolution [58][59][60].…”

Section: Back-projectionmentioning

confidence: 99%

Enhanced Back-Projection as Postprocessing for Pansharpening

Liu

Fei

et al. 2019

Remote Sensing

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Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods.

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First-order derivative-based super-resolution

Cited by 18 publications

References 14 publications

Deep Back-Projection Networks for Super-Resolution

Deep Back-Projection Networks for Super-Resolution

Image Restoration for the MRA-Based Pansharpening Method

Enhanced Back-Projection as Postprocessing for Pansharpening

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