Robust flash denoising/deblurring by iterative guided filtering

Seo, Hae Jong; Milanfar, Peyman

doi:10.1186/1687-6180-2012-3

Cited by 24 publications

(14 citation statements)

References 30 publications

(76 reference statements)

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“…Usually, this iteration is performed only once. While [8] employs an iterative framework using GF. In this case, a blurred image can also be used as the input image.…”

Section: )) and Substituting The Corresponding Values Into H We Getmentioning

confidence: 99%

Local color distribution projection filtering using a homogeneous coordinate system

Iwata¹,

Shirai²,

Okuda

2013

2013 IEEE International Conference on Image Processing

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In this paper, we present a denoising method of using multiple image composition based on the color transform of local color distribution of patches. In the method, the color of a guidance image is transformed so as to get close to a noisy input image by a patch-wise color transform. As a result, a noiseless image is obtained. Actually this method can be regarded as the generalized version of our previous method [1] and the guided filter [2], and gives improved images with more clear appearance. The existing methods have a restriction that a filter output form is expressed only by a linear transform, i.e., a first order polynomial. In this paper, we introduce a more general formula for this category of filtering with the guidance image. We deal with a quadratic formula for realizing more flexible transform instead of the previous linear transform, and moreover introduce more simple and flexible regularization.

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“…Usually, this iteration is performed only once. While [8] employs an iterative framework using GF. In this case, a blurred image can also be used as the input image.…”

Section: )) and Substituting The Corresponding Values Into H We Getmentioning

confidence: 99%

Local color distribution projection filtering using a homogeneous coordinate system

Iwata¹,

Shirai²,

Okuda

2013

2013 IEEE International Conference on Image Processing

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“…In filtering, i.e., guided filter [10] addressed a variety of applications in the field of computer vision/graphics such as haze removal, image fusion, and flash denoising/deblurring [10,14,15]. Motivated by effectiveness of the LP in the previous work, this letter theoretically analyzes performance of the LP first to find its properties with presence of noise and then applies it as texture-transferring for color image denoising.…”

Section: Proposed Color Ima Ge Denoisingmentioning

confidence: 98%

Color Image Denoising via Cross-Channel Texture Transferring

Dinh

Canh

2016

IEEE Signal Process. Lett.

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Image denoising can reduce the perturbation inevitably generated during image signal acquisition and its subsequent processing. While the utilization of nonlocal properties can enhance the performance of the state-of-the-art denoising me thods, a heavy computational burden is incurred especially for color images. Inspired by the high correlation in the texture information over color channels, for a reduction of the computational burden, this letter proposes denoising the luma channel first and then performing a patch-wise linear prediction to transfer the texture information of the denoised luma channel to the other two channels. The texture transferring is adapted to local characteristic (i.e., variance of the local patches) for a reduction of color smearing caused by large prediction error especially along edges. Experimental results confirm that the proposed method achieves performance improvement over the state-of-the-art color image denoising me thods only at a slightly increased complexity of single-channel denoising. Index Terms-color image denoising, texture transferring, linear prediction, color smearingThe authors are with the Co llege of Information and Co mmunicat ion Engineering, Sungkyunkwan Un iversity,

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“…Since the guided filter performs well in terms of quality and efficiency, it has been applied to enhancement, denoising, and haze removal [6,14,23]. Its main idea is to filter input images by considering the content of the guidance image.…”

Section: Guided Filtermentioning

confidence: 99%

A new artistic information extraction method with multi channels and guided filters for calligraphy works

Zheng

Miao

Shi

et al. 2015

Multimed Tools Appl

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The artistic beauty of Chinese calligraphy is constituted by two elements: form and spirit . To learn and study calligraphy works, both of form and spirit should be extracted correctly. However, most currently used calligraphy image extraction methods can only obtain form information. To address this problem, an extraction method, based on multi-channel and guided filters, is proposed in this study. The proposed method consists of three major operations: color space transformation, tablet and writing discrimination, and information extraction using guided filter. To simulate the human visual perception of calligraphy work, the color space of a calligraphy image is converted from RGB to CIELAB firstly. Then the calligraphy image is distinguished as either a tablet or a writing based on channel b. Finally, information extraction using guided filter is performed. For a tablet, a two-stage guided filtering strategy based on L channel is employed to reduce noise and obtain form information. For a writing, a guided filter based on channels L and a is used to extract both form and spirit information. To demonstrate the accuracy and efficiency of the proposed method, comparison experiments are implemented on both types of images. Experimental results reveal the advantages of the proposed method.

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Robust flash denoising/deblurring by iterative guided filtering

Cited by 24 publications

References 30 publications

Local color distribution projection filtering using a homogeneous coordinate system

Local color distribution projection filtering using a homogeneous coordinate system

Color Image Denoising via Cross-Channel Texture Transferring

A new artistic information extraction method with multi channels and guided filters for calligraphy works

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