Semi-supervised Learning to Remove Fences from a Single Image

Shang, Wei; Zhu, Pengfei; Ren, Dongwei

doi:10.1007/978-3-030-60633-6_7

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In [87], authors employed three UNNPs to model three different image components, i.e., background, fence, and fence mask, in the fence removal problem. The initial mask was estimated using a recurrent network, which was then further refined using an UNNP network that uses a Laplacian smoothness loss function.…”

Section: Image Decompositionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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In recent years, advancements in machine learning (ML) techniques, in particular, deep learning (DL) methods have gained a lot of momentum in solving inverse imaging problems, often surpassing the performance provided by hand-crafted approaches. Traditionally, analytical methods have been used to solve inverse imaging problems such as image restoration, inpainting, and superresolution. Unlike analytical methods for which the problem is explicitly defined and the domain knowledge is carefully engineered into the solution, DL models do not benefit from such prior knowledge and instead make use of large datasets to predict an unknown solution to the inverse problem. Recently, a new paradigm of training deep models using a single image, named untrained neural network prior (UNNP) has been proposed to solve a variety of inverse tasks, e.g., restoration and inpainting. Since then, many researchers have proposed various applications and variants of UNNP. In this paper, we present a comprehensive review of such studies and various UNNP applications for different tasks and highlight various open research problems which require further research.

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Section: Image Decompositionmentioning

confidence: 99%

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey

Qayyum

Ilahi

Shamshad

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Restoration homographic matrices, dictionary based on k-means labeling, exemplar based inpainting manual initialization Deep learning based methods [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] De-fencing adversarial, structural [21], [22], [23], [24], [25] Fusion multi-scale decomposition, dictionary-learning, nuclear norm regularizer, morphologies constraints, adaptive fusion rules, fractional differential coefficients, geometric sparse coefficients overcomplete dictionary, patch based clustering, single dictionary learning time efficiency, separate fusion and noise removal tasks, information loss due to channel-wise processing Model based methods [26], [27], [28], [29], [30], [31], [32], [33] [34], [35], [36], [37] Fusion SSIM, encoder features, K-means clustering, NSCT, Coupled-Neural-Ps consistency verification photo realistic fusion, blocking artifacts, post processing complete contours; and generator-discriminator setting for prediction of contour completion. In [3], subtraction based on gray-scale binarization is applied on multi-focus auxiliary images to obtain initial mask for image inpainting.…”

Section: Schemesmentioning

confidence: 99%

“…Coarsest level feature representation and motion vectors are used to reconstruct the background image using residual learning network. In [17], long short-term memory cells based supervised recurrent network with asymmetric loss function is employed to detect fences.…”

Section: Schemesmentioning

confidence: 99%

De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting

2022

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Multi-focus image fusion aims at combining source information from differently focused images. Fusion of multi-focus images has great applications in machine vision. The paper focuses on removal of fence occlusions in multi-focus images. The proposed model extracts fence occlusion map using salient image features, and refined by morphological operators. Binary operators, and inpainting methods are used for fence removal and restoration. The proposed model estimates the fence area using statistical characteristics in focused regions. Similarly, binary filtering is used to perform thinning of enlarged areas for optimised restoration. The proposed model employs guided filtering for consistency verification. Fusion and restoration results are compared using several (reference and no-reference based) image quality metrics. Simulations show that proposed scheme achieves better results (visually and quantitatively) as compared to existing state-of-the-art techniques.

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“…For fence removal from an image, Shang et al [40] proposed a supervised learning-based method to detect the mask of the fences and then employed unsupervised learning for the robust restoration of the background image free from the fences. They used three UNNP networks to model the three different parts of an image, i.e., background image, fences layer, and fences mask.…”

Section: Image Decompositionmentioning

confidence: 99%