Efficient improvement method for separation of reflection components based on an energy function

Yamamoto, Takahisa; Kitajima, Toshihiro; Kawauchi, Ryota

doi:10.1109/icip.2017.8297078

Cited by 11 publications

(22 citation statements)

References 17 publications

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“…As can be seen, our method produces visually natural-looking results similar to the ground truths, while the compared methods [YCK06,TI05] induce visual artifacts including enhanced textures/structures and color distortion. In addition, we note that the structures of the diffuse image are slightly boosted in the results of [YKK17].…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 71%

“…An example is shown in Figure 4. Yamamoto et al [YKK17] may enhance the facial details, e.g.the beard on the face and background, so does Yoon et al [YCK06]. Shen et al [SZSX08] degrades the texture details, leading to unnatural-looking result.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Shen et al [SZSX08] degrades the texture details, leading to unnatural-looking result. Tan et al [TI05] wrongly alters the il- [YKK17], Yoon et al [YCK06], and Tan et al [TI05], respectively. Please zoom in on the results and their corresponding close-ups for more details.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…As shown, Akashi et al [AO15] fails to remove noises since their employed NMF model is sensitive to outliers. Yamamoto et al [YKK17] can effectively remove noise, but induce specular highlight residual in the resulting diffuse image. Other clustering-based methods [SC09,YCK06] are sensitive to noise and thus produce visually unpleasing results.…”

Section: Robustness To Noisementioning

confidence: 99%

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Specular Highlight Removal for Real‐world Images

Zhang

Song

et al. 2019

Computer Graphics Forum

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Removing specular highlight in an image is a fundamental research problem in computer vision and computer graphics. While various methods have been proposed, they typically do not work well for real‐world images due to the presence of rich textures, complex materials, hard shadows, occlusions and color illumination, etc. In this paper, we present a novel specular highlight removal method for real‐world images. Our approach is based on two observations of the real‐world images: (i) the specular highlight is often small in size and sparse in distribution; (ii) the remaining diffuse image can be represented by linear combination of a small number of basis colors with the sparse encoding coefficients. Based on the two observations, we design an optimization framework for simultaneously estimating the diffuse and specular highlight images from a single image. Specifically, we recover the diffuse components of those regions with specular highlight by encouraging the encoding coefficients sparseness using L0 norm. Moreover, the encoding coefficients and specular highlight are also subject to the non‐negativity according to the additive color mixing theory and the illumination definition, respectively. Extensive experiments have been performed on a variety of images to validate the effectiveness of the proposed method and its superiority over the previous methods.

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 71%

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

Section: Robustness To Noisementioning

confidence: 99%

See 2 more Smart Citations

Specular Highlight Removal for Real‐world Images

Zhang

Song

et al. 2019

Computer Graphics Forum

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show abstract

“…. From left to right: the inputs, ground-truth diffuse images, results of ours, Spec-GAN [Funke et al 2018], [Yamamoto et al 2017], and [Shen and Cai 2009], respectively.…”

Section: Neural Networkmentioning

confidence: 99%