A Pseudo-logarithmic Image Processing Framework for Edge Detection

Vertan, Constantin; Oprea, Alina; Florea, Corneliu; Florea, Laura

doi:10.1007/978-3-540-88458-3_57

Cited by 23 publications

(14 citation statements)

References 5 publications

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“…Based on a new imaging device model [28], a generalized LIP (GLIP) model has been developed [29]. Other extensions of the LIP model include the parametric [21,30], the pseudo and the harmonic LIP models [31,32], and the symmetric extension [33]. The LR model has also been recently extended from two perspectives: the Bregman divergence [34] and the triangular norm [6].…”

Section: A Brief Reviewmentioning

confidence: 99%

A generalized gamma correction algorithm based on the SLIP model

Deng

2016

EURASIP J. Adv. Signal Process.

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Traditional contrast enhancement techniques were developed to enhance the dynamic range of images with narrow histograms. However, it is not unusual that an image with a broad histogram still suffers from low contrast in both the shadow and highlight areas. In this paper, we first develop a unified framework called the generalized gamma correction for the enhancement of these two types of images. The generalization is based on the interpretation of the gamma correction algorithm as a special case of the scalar multiplication of a generalized linear system (GLS). By using the scalar multiplication based on other GLS, we obtain the generalized gamma correction algorithm. We then develop an algorithm based on the generalized gamma correction algorithm which uses the recently developed symmetric logarithmic image processing (SLIP) model. We demonstrate that the proposed algorithm can be configured to enhance both types of images by adaptively choosing the mapping function and the multiplication factor. Experimental results and comparisons with classical contrast enhancement and state-of-the-art adaptive gamma correction algorithms demonstrate that the proposed algorithm is an effective and efficient tool for the enhancement of images with either narrow or broad histogram.

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Section: A Brief Reviewmentioning

confidence: 99%

A generalized gamma correction algorithm based on the SLIP model

Deng

2016

EURASIP J. Adv. Signal Process.

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“…First, the cone-space structure was completed to a vector space structure (Pȃtraşcu and Voicu, 2000), while more recently the generative logarithmic function was replaced by a logarithmic-like one with the benefits of a simplified calculus (Vertan et al, 2008). This extension preserves the same behavior, yet it differs in the nature of the generative function.…”

Section: Resemblance With the Weber-fechner Perception Lawmentioning

confidence: 99%

“…where u is enforced to be larger than v, u > v. The models mostly used in image processing (Jourlin and Pinoli, 1988;Pȃtraşcu and Voicu, 2000;Vertan et al, 2008) are summarized in Table 1.…”

Section: Resemblance With the Weber-fechner Perception Lawmentioning

confidence: 99%

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Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

Florea

2013

International Journal of Applied Mathematics and Computer Science

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While most of state-of-the-art image processing techniques were built under the so-called classical linear image processing, an alternative that presents superior behavior for specific applications comes in the form of Logarithmic Type Image Processing (LTIP). This refers to mathematical models constructed for the representation and processing of gray tones images. In this paper we describe a general mathematical framework that allows extensions of these models by various means while preserving their mathematical properties. We propose a parametric extension of LTIP models and discuss its similarities with the human visual system. The usability of the proposed extension model is verified for an application of contrast based auto-focus in extreme lighting conditions. The closing property of the named models facilitates superior behavior when compared with state-of-the-art methods.

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“…While a linear combination of arithmetic operation and classical LIP isomorphic formation can also expressed in (11), where k is the weight of classical LIP, b 1 and b 2 are the weights of the arithmetic operation, respectively.…”

Section: The Interpretation Of Parameter λmentioning

confidence: 99%

Single Parameter Logarithmic Image Processing for Edge Detection

Ren

Chen

2013

IEICE Trans. Inf. & Syst.

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SUMMARYConsidering the non-linear properties of the human visual system, many non-linear operators and models have been developed, particularly the logarithmic image processing (LIP) model proposed by Jourlin and Pinoli, which has been proved to be physically justified in several laws of the human visual system and has been successfully applied in image processing areas. Recently, several modifications based on this logarithmic mathematical framework have been presented, such as parameterized logarithmic image processing (PLIP), pseudo-logarithmic image processing, homomorphic logarithmic image processing. In this paper, a new single parameter logarithmic model for image processing with an adaptive parameter-based Sobel edge detection algorithm is presented. On the basis of analyzing the distributive law, the subtractive law, and the isomorphic property of the PLIP model, the five parameters in PLIP are replaced by a single parameter to ensure the completeness of the model and physical constancy with the nature of an image, and then an adaptive parameterbased Sobel edge detection algorithm is proposed. By using an image noise estimation method to evaluate the noise level of image, the adaptive parameter in the single parameter LIP model is calculated based on the noise level and grayscale value of a corresponding image area, followed by the singleparameter LIP-based Sobel operation to overcome the noise-sensitive problem of classical LIP-based Sobel edge detection methods, especially in the dark area of an image, while retaining edge sensitivity. Compared with the classical LIP and PLIP model, the given single parameter LIP achieves satisfactory results in noise suppression and edge accuracy. key words: image edge detection, single parameter logarithmic image processing, image noise measurement

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A Pseudo-logarithmic Image Processing Framework for Edge Detection

Cited by 23 publications

References 5 publications

A generalized gamma correction algorithm based on the SLIP model

A generalized gamma correction algorithm based on the SLIP model

Parametric logarithmic type image processing for contrast based auto-focus in extreme lighting conditions

Single Parameter Logarithmic Image Processing for Edge Detection

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