Exact global histogram specification optimized for structural similarity

Avanaki, Alireza Nasiri

doi:10.1007/s10043-009-0119-z

Cited by 63 publications

(41 citation statements)

References 19 publications

(51 reference statements)

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“…In this paper, we propose a specialized variational method that enables us to order in a strict way the pixel 3 values of a digital image by slightly reducing the quantization noise that they contain. A sketch of our approach was given in a conference paper [11].…”

Section: −1 Tmentioning

confidence: 99%

“…This approach tends to amplify any noise since noise in a smooth region may be mistaken as an edge and hence is sharpened. Post-processing approach or iterative methods can be applied to suppress the amplified noises [3].…”

Section: Sorting Algorithmsmentioning

confidence: 99%

“…For our pixel sorting method, we minimize J as defined by (3) where N i is the set of the four adjacent neighbors and…”

Section: B Implementation Of Algorithmmentioning

confidence: 99%

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Exact Histogram Specification for Digital Images Using a Variational Approach

2012

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We consider the problem of exact histogram specification for digital (quantized) images. The goal is to transform the input digital image into an output (also digital) image that follows a prescribed histogram. Classical histogram modification methods are designed for real-valued images where all pixels have different values, so exact histogram specification is straightforward. Digital images typically have numerous pixels which share the same value. If one imposes the prescribed histogram to a digital image, usually there are numerous ways of assigning the prescribed values to the quantized values of the image. Therefore, exact histogram specification for digital images is an ill-posed problem. In order to guarantee that any prescribed histogram will be satisfied exactly, all pixels of the input digital image must be rearranged in a strictly ordered way. Further, the obtained strict ordering must faithfully account for the specific features of the input digital image. Such a task can be realized if we are able to extract additional representative information (called auxiliary attributes) from the input digital image. This is a real challenge in exact histogram specification for digital images. We propose a new method that efficiently provides a strict and faithful ordering for all pixel values. It is based on a well designed variational approach. Noticing that the input digital image contains quantization noise, we minimize a specialized objective function whose solution is a real-valued image with slightly reduced quantization noise, which remains very close to the input digital image. We show that all the pixels of this real-valued image can be ordered in a strict way with a very high probability. Then transforming the latter image into another digital image satisfying a specified histogram is an easy task. Numerical results show that our method outperforms by far the existing competing methods.

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Section: −1 Tmentioning

confidence: 99%

Section: Sorting Algorithmsmentioning

confidence: 99%

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Exact Histogram Specification for Digital Images Using a Variational Approach

2012

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“…The wavelet-based approach tends to amplify the noise since a noise in a smooth region may be mistaken as an edge and hence is sharpened. Post-processing approach or iterative methods can be applied to suppress the amplified noises [2]. We emphasize that both the local mean approach and the wavelet-based approach cannot realize strict ordering without degrading the input quantized image.…”

Section: −1 Tmentioning

confidence: 99%

A Variational Approach for Exact Histogram Specification

Chan

Nikolova

Wen

2012

Lecture Notes in Computer Science

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Abstract. We focus on exact histogram specication when the input image is quantified. The goal is to transform this input image into an output image whose histogram is exactly the same as a prescribed one. In order to match the prescribed histogram, pixels with the same intensity level in the input image will have to be assigned to different intensity levels in the output image. A novel method enabling to order strictly all pixel values was proposed to solve the problem by using auxiliary attributes to classify pixels with the same intensity value. Local average intensities and wavelet coefficients have been used by the past as the second attribute. However, these methods cannot enable strict-ordering without degrading the image. In this paper, we propose a variational approach to establish an image preserving strict-ordering of the pixel values. We show that strict-ordering is achieved with probability one. Our method is image preserving in the sense that it reduces the quantization noise in the input quantified image. Numerical results show that our method gives better quality images than the preexisting methods.

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“…It relies on the assumption that successful correction of an erroneous co-registration leads to increased similarity between the reference and target image. As a measure for image similarity, the Mean Structural Similarity Index (MSSIM) has been chosen, as it is reported to be highly sensitive to even marginal image displacements [35,36].…”

Section: Validation Of Calculated Spatial Shiftsmentioning

confidence: 99%

AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

et al. 2017

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Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter-or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi-or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

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Exact global histogram specification optimized for structural similarity

Cited by 63 publications

References 19 publications

Exact Histogram Specification for Digital Images Using a Variational Approach

Exact Histogram Specification for Digital Images Using a Variational Approach

A Variational Approach for Exact Histogram Specification

AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

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