Dual-Channel Contrast Prior for Blind Image Deblurring

Yang, Dayi; Wu, Xiaojun

doi:10.1109/access.2020.3045857

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…Han et al 39 defined the patch-wise maximal pixels prior (PMaxP) based on the fact that the PMaxP is unlikely to remain maximal after image blurring. Yang and Wu 40 proposed a dual-channel contrast prior (Dual-CP) that models contrast by the difference between dark and bright channels. In particular, our proposed DPP prior is based on the fact that the difference value of the DPP decreases significantly with the turbulent blurring process.…”

Section: Blind Deblurring Methodsmentioning

confidence: 99%

Blind turbulent image deblurring through dual patch-wise pixels prior

2023

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Deblurring turbulent images is an active topic in image processing and low-level vision research. Existing methods usually use the parametric physical model for nonblind image restoration, which lacks adaptability to different turbulent scenes. To overcome this challenge, a dual patch-wise pixels (DPP) prior is proposed for effective blind deblurring of turbulent images. A DPP-based turbulent image deblurring model was established based on the fact that the value of the DPP decreases through the turbulent blurring process, which has been proven both mathematically and experimentally. To solve the nonlinear DPP in the model, a linear mapping operator was constructed. Additionally, half-quadratic splitting and threshold methods were used to solve the L0 regularization term. Experimental results showed that the proposed algorithm performs well on various types of turbulent scenes as well as real images and outperforms state-ofthe-art algorithms in terms of computational efficiency and effectiveness.

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Section: Blind Deblurring Methodsmentioning

confidence: 99%

Blind turbulent image deblurring through dual patch-wise pixels prior

2023

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“…By combining the dark channel and light channel, Wen et al [4] proposed the 0 L regularization blur kernel estimation method that can measure the similarity between adjacent kernels. Yang et al [31] analyzed the influence of blur kernel on the image contrast and proposed a dual channel contrast prior algorithm. Hsieh et al [5] proposed an imposed zero patch minimum constraint in the blind deblurring model.…”

Section: Related Workmentioning

confidence: 99%

Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors

Yang

Cheng

et al. 2023

IEEE Access

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The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel. Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information. We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors. Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images. The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image. Meanwhile, we consider the influence of the brightness feature of the restored blur kernel. By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel. The experimental results show that our algorithm has good results in the restoration of blur kernels and text images.INDEX TERMS Blind text image deblurring, sparse priors, multi-scale fusion, wavelet transform.

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“…Yan et al [15] combined dark channel and bright channel and designed an extreme channel prior algorithm. Since then, Yang [32] and Ge [16] have made further improvements to the problems faced by the extreme channel prior algorithms. At the same time, the blind image deblurring algorithms, based on local prior information, have also made significant achievements, i.e., the method based on the local maximum gradient (LMG) prior proposed by Chen et al [25] and the method based on the local maximum difference (LMD) prior proposed by Liu et al [33].…”

Section: Image Priors-based Algorithmsmentioning

confidence: 99%

Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior

et al. 2022

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In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images.

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Dual-Channel Contrast Prior for Blind Image Deblurring

Cited by 7 publications

References 41 publications

Blind turbulent image deblurring through dual patch-wise pixels prior

Blind turbulent image deblurring through dual patch-wise pixels prior

Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors

Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior

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