Local contrast enhancement

Bressan, Marco; Dance, Christopher R.; Poirier, Hervé; Arregui, Damián

doi:10.1117/12.724721

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Even though this adjusts the contrast globally in an image, there are still contrast variations in portions of the image. Local contrast enhancement algorithms [55] or adaptive histogram equalisation [56] can be used to normalise the images for variations in the local contrast and local luminance. 2.…”

Section: Layermentioning

confidence: 99%

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

Antony

McGuinness

Moran

et al. 2020

Intelligent Systems Reference Library

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Feature learning refers to techniques that learn to transform raw data input into an effective representation for further higher-level processing in many computer vision tasks. This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from radiographs (X-ray images). Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the region of interest that is to detect and extract the knee joint regions from the radiographs, and the next step is to classify the localized knee joints based on a radiographic classification scheme such as Kellgren and Lawrence grades. First, the existing approaches for detecting (or localizing) the knee joint regions based on handcrafted features are reviewed and outlined in this chapter. Next, three new approaches are introduced: 1) to automatically detect the knee joint region using a fully convolutional network, 2) to automatically assess the radiographic knee OA using CNNs trained from scratch for classification and regression of knee joint images to predict KL grades in ordinal and continuous scales, and 3) to quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross entropy and mean-squared error using multi-objective convolutional learning. The results from these methods show progressive improvement in the overall quantification of the knee OA severity. Two public datasets: the OAI and the MOST are used to evaluate the approaches with promising results that outperform existing approaches. In summary, this work primarily contributes to the field of automated methods for localization (automatic detection) and quantification (image classification) of radiographic knee OA.

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Section: Layermentioning

confidence: 99%

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

Antony

McGuinness

Moran

et al. 2020

Intelligent Systems Reference Library

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“…cl. [2] introduced an algorithm based on local contrast enhancement. This algorithm focuses on locally enhancing contrast in under exposed image regions using a background-based approach.…”

Section: Input Gray Levelmentioning

confidence: 99%

A Comprehensive Method for Image Contrast Enhancement Based on Global –Local Contrast and Local Standard Deviation

Singh¹

2014

IJRET

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Image contrast enhancement is used to increase the detailed visibility of an image by performing some transformation on input image. Global contrast enhancement and local contrast enhancement both are the very popular method for contrast enhancement. But these two methods are also not sufficient in order to enhance an image when image has some constant area and it is not possible to perform any kind of transformation to neighbor hooding pixels of the central pixel. Due to that some information may be lost. This paper proposed a method to remove the divided by zero condition, arises due to the local standard deviation of that constant area in order to enhance an image. This method enhances an image without any loss in detailed visibility of an image.

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“…For this purpose, the decision mechanism uses statistics such as noise measures or luminance and chrominance distributions on a low resolution version of the image. Details for particular components of the XAIE decision mechanism can be found in (Eschbach and Fuss, 1999;Bressan et al, 2007). The settings for the decision mechanism are largely based on user preference evaluations.…”

Section: Image Enhancementmentioning

confidence: 99%

TOWARDS INTENT DEPENDENT IMAGE ENHANCEMENT - State-of-the-art and Recent Attempts

Bressan¹,

Favre²

2007

Proceedings of the Second International Conference on Computer Vision Theory and Applications

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Image enhancement is mostly driven by intent and its future largely relies on our ability to map the space of intentions with the space of possible enhancements. Taking into account the semantic content of an image is an important step in this direction where contextual and aesthetic dimensions are also likely to have an important role. In this article we detail the state-of-the-art and some recent efforts in for semantic or content-dependent enhancement. Through a concrete example we also show how image understanding and image enhancement tools can be brought together. We show how the mapping between semantic space and enhancements can be learnt from user evaluations when the purpose is subjective quality measured by user preference. This is done by introducing a discretization of both spaces and notions of coherence, agreement and relevance to the user response. Another example illustrates the feasibility of solving the situation where the binary option of whether or not to enhance is considered.

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Local contrast enhancement

Cited by 5 publications

References 16 publications

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

A Comprehensive Method for Image Contrast Enhancement Based on Global –Local Contrast and Local Standard Deviation

TOWARDS INTENT DEPENDENT IMAGE ENHANCEMENT - State-of-the-art and Recent Attempts

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