Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction

Yang, Qi; Zhang, Yanzhu; Zhao, Tiebiao; Chen, YangQuan

doi:10.1016/j.isatra.2017.03.001

Cited by 24 publications

(8 citation statements)

References 40 publications

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“…[40][41][42][43][44]. The fractional differential operator has the capability of not only preserving high frequency contour features, but also improving the low-frequency texture details in smooth region [45]. Therefore, in this paper, we defined a general version of the Hessian-matrix, named fractional-order Hessian-matrix (FrH) for feature detection.…”

Section: Multi-scale Fractional-order Hessian Matrixmentioning

confidence: 99%

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

Fan

Zong

Tang

et al. 2022

IET Image Processing

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For image contrast enhancement operation, it is a keypoint to obtain more natural enhanced results and keep more details without distortion. In this paper, a novel image Hessian-matrix weighted histogram for image contrast enhancement is proposed, which can improve the contrast of smooth regions and simultaneously restrain the contrast of texture regions. In the proposed method, the multi-scale fractional-order Hessian-matrix is firstly utilized to detect and quantify the texture information of the input image, which explores the regions that should be contrasted or should be restrained. Then, the strong texture regions are suppressed by a designed suppress function. Finally, the information on unsuppressed regions and suppressed texture regions will be count by a histogram, which is termed as Hessian-matrix weighted Histogram (HessHist) in this paper. According to HessHist, the corresponding cumulative distribution function will realize the contrast enhancement operation on the input image. For real-time application, the integral images are introduced for fast computation of the HessHist. Experimental results show that the proposed HessHist-based image enhancement algorithm preserves more details of input image without distortion, and is competitive with state-of-the-art image enhancement algorithms in both subjective visual perception and objective evaluation metrics.

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Section: Multi-scale Fractional-order Hessian Matrixmentioning

confidence: 99%

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

Fan

Zong

Tang

et al. 2022

IET Image Processing

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show abstract

“…Chang et al [31] proposed a SRR method based on locally linear embedding manifold learning, but its reconstruction effect had the problem of the over-fitting or under-fitting. In [32]and [33], the super-resolution methods based on variation were proposed, which could produce the rich details and sharp edges. Yang et al [34] proposed a SRR method based on SR.…”

Section: B Single Image Srrmentioning

confidence: 99%

Joint Framework for Image Fusion and Super-Resolution via Multicomponent Analysis and Residual Compensation

2019

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To solve the problems in two-step processing of image fusion and Super-Resolution Reconstruction (SRR), we propose a joint framework of image Fusion and Super-Resolution (FSR) based on multicomponent analysis and residual compensation. Inspired by the idea of multicomponent analysis, we design a new structure-texture decomposition model to realize multicomponent dictionary learning for the above task. To depict the relationship between low-resolution image and its corresponding high-resolution image, the correlation of their sparse coding coefficients is introduced in the model. To compensate the information loss during the SRR, we propose a reconstruction residue compensation mechanism, in which the reconstruction residual error is compensated into the initial result of FSR to improve the quality of the final processing result. In addition, we propose different fusion algorithms for structure and texture components. For structure components, the fusion rule with the maximum absolute value is adopted, and for texture components, we design a new saliency measure to construct fusion results. Experiment results show that the proposed method can well retain the brightness and detail information in the original image, and is superior to other methods in subjective and objective evaluation.

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“…As multi-scale pyramid is built using up scaling factor of 2 , to achieve scale factors of 2,4,8 is quite time consuming task and actually there are no prominent variations in the image structure with such small step size. Average PSNR of [19] when compared with existing SR methods like [20], [21] is improved on four benchmark datasets viz. Set5, Set14, BSD500 & UIUC, but PSNR of [22] was found to be more.…”

Section: A Techniques For Single Image Super Resolution (Sisr)mentioning

confidence: 99%

A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

Borse*¹,

Patil²

2019

IJRTE

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Now-a-days many applications dealing with visual content need to access underlying details in the image or video of interest. For instance, detailing is required to take life critical decisions for further action plans by a doctor. Clarity and structural information are some of the aspects of detailing. It can be achieved by cost effective software solution like super resolution reconstruction of an image. Super resolution (SR) deals in increasing resolution of an image to make it more clear and valid for use. Many SR techniques exist with variable goals to achieve. With this intension a new technique for preserving structural information in the reconstruction process is proposed. The system extends a deep convolution neural network by adding a new optimization layer at the end of network activation layer. This new layer maintains permissible error threshold in the acquired signal and tries to improve the signal by feeding back latest reconstructed frame. The proposed system shows noticeable improvement in structural similarity of reconstructed images as compared with the ground truth.

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Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction

Cited by 24 publications

References 40 publications

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

HessHist: A Hessian‐matrix weighted histogram for image contrast enhancement

Joint Framework for Image Fusion and Super-Resolution via Multicomponent Analysis and Residual Compensation

A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution

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