Structure extraction from texture via relative total variation

Liu, Xü; Yan, Qing; Xia, Yang; Jia, Jiaya

doi:10.1145/2366145.2366158

Cited by 627 publications

(655 citation statements)

References 42 publications

(46 reference statements)

Supporting

Mentioning

648

Contrasting

Unclassified

Order By: Relevance

“…To improve the accuracy of the initial atmospheric veil and alleviate the block artifacts, a refinement operation requires to be performed on V 0 (x) for simultaneously smoothing the textural regions and preserving the depth abrupt edges. Inspired by [13], we extract the main structure of the hazy images and find that the main structure is very similar to the depth discontinuity regions. Therefore, based on this observation, we minimize the following optimization energy function to generate the structure-aware atmospheric veil V(x):…”

Section: Atmospheric Veil Refinementmentioning

confidence: 99%

“…(7), the first part is the fidelity term, which can prevent the solution departing too much from V 0 (x), and the second part enforces the structure-aware constraint on V 0 (x), which can smooth the textural details and preserve the overall structure. Inspired by relative total variation (RTV) [13], the weight matrix W can be set as:…”

Section: Atmospheric Veil Refinementmentioning

confidence: 99%

See 1 more Smart Citation

Single Image Haze Removal Using Structure-Aware Atmospheric Veil

Liu

Chen²,

Shang

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYIn this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At last, the haze-free image can be restored by inverting the atmospheric scattering model. Additionally, brightness adjustment is also performed for preventing the dehazing result too dark. Experimental results on hazy images reveal that the proposed method can effectively remove the haze and yield dehazing results with vivid color and high scene visibility.

show abstract

Section: Atmospheric Veil Refinementmentioning

confidence: 99%

Section: Atmospheric Veil Refinementmentioning

confidence: 99%

Single Image Haze Removal Using Structure-Aware Atmospheric Veil

Liu

Chen²,

Shang

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Signal obtained from an image scanline to illustrate the effect of our edge-aware constraints. As the red arrow indicates, with our edge-aware constraints, we can better preserve edges than [Xu et al 2012] [35] while maintaining similar image filtering effect.…”

Section: Introductionmentioning

confidence: 99%

“…ing [11,3,34,35], sparse data interpolation over images [21,24], image inpainting [27], seamless cloning and compositing [26,9] and matting [31].…”

Section: Introductionmentioning

confidence: 99%

Edge-Aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation

Hua

Bie

Zhang

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

View full text Add to dashboard Cite

Gradient domain methods are popular for image processing. However, these methods even the edge-preserving ones cannot preserve edges well in some cases. In this paper, we present new constraints explicitly to better preserve edges for general gradient domain image filtering and theoretically analyse why these constraints are edge-aware. Our edge-aware constraints are easy to implement, fast to compute and can be seamlessly integrated into the general gradient domain optimization framework. The improved framework can better preserve edges while maintaining similar image filtering effects as the original image filters. We also demonstrate the strength of our edge-aware constraints on various applications such as image smoothing, image colorization and Poisson image cloning.

show abstract

“…The prior MAP based approaches that are successful can be roughly classified into two types i.e. those with explicit edge prediction steps like using a shock filter [2,3,4,5,6,7,8] and those which include implicit regularization process [9,10]. The common factor is the intermediate image consisting of only step like structures called the unnatural representation of the image, which is the key factor in the success of MAP based methods.…”

Section: Iintroductionmentioning

confidence: 99%

Handling Noise and Outliers in Single Image Deblurring using L0 Sparsity

Ashok¹

2015

IJAREEIE

View full text Add to dashboard Cite

Camera shake during exposure leads to image blur and poses an important problem in digital photography. Blind deconvolution recovers the sharp original image from a blurred image. MAP has been the most widely used deconvolution field but naive MAP methods mostly tends to favour no-blur solution. An intermediate representation of the image called unnatural representation has been found to the main reason for success of existing MAP based methods. This paper presents a new blind deconvolution algorithm based on L 0 sparsity for single image motion deblurring, which is robust to the presence of noise. Directional filtering is used for noise handling process owing to the fact that it does not interfere with blur of the image affecting the kernel estimation process adversely. Also, a non-blind deconvolution step with explicit outlier handling is incorporated in final image restoration step which ensures that the image is devoid of ringing artifacts and handles other non-linearities. This presents an efficient optimization problem which requires only a few iterations solve and provides very good visual quality images with no ringing artifacts despite the presence of noise. The results are comparable to state-of-the-art methods dealing with images affected with only blur while being robust to noise unlike state-of-the-art methods. KEYWORDS:Camera shake, motion blur,L 0 sparsity, directional filtering, outlier handling, ringing artifacts. I.INTRODUCTIONMotion blur is a common artifact in digital photography which produces blurry images with inevitable information loss. Taking handheld photographs in low light condition is a challenging process. Higher exposure times are needed due to the lesser availability of light in this case and photos end up blurry because the photographer moves during exposure time. Usage of the tripod and mechanical stabilization of lens or sensors is a likely solution, but transportation issues and other factors limit the usefulness and longer exposure time remains a challenging problem. Increasing light sensitivity of the camera using a higher ISO setting can reduce the exposure time but a trade off with noise exists in this case. Reduction in the exposure time comes at the cost of increase in the noise levels but the noise levels still remains considerably high for handheld photography. As a result, the photos end up being blurry and noisy. The process of recovering the original image from a blurred image has been a fundamental research problem of major interest in the field of digital image processing. The recent years have witnessed significant advances in the development of single image deblurring techniques. Image deblurring techniques can be primarily divided into two types-blind image deblurring and non-blind image deblurring. In non-blind deconvolution process, the motion blur kernel is assumed to be known and we have to just estimate the latent image. Blind deconvolution is a much more ill posed problem ,as both the blur kernel that has caused the blurring and the latent imag...

show abstract

Structure extraction from texture via relative total variation

Cited by 627 publications

References 42 publications

Single Image Haze Removal Using Structure-Aware Atmospheric Veil

Single Image Haze Removal Using Structure-Aware Atmospheric Veil

Edge-Aware Gradient Domain Optimization Framework for Image Filtering by Local Propagation

Handling Noise and Outliers in Single Image Deblurring using L0 Sparsity

Contact Info

Product

Resources

About