Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results

Sliced Wasserstein Distance for Learning Gaussian Mixture Models

Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters. However, EM guarantees only convergence to a stationary point of the log-likelihood function, which could be arbitrarily worse than the optimal solution. Inspired by the relationship between the negative log-likelihood function and the Kullback-Leibler (KL) divergence, we propose an alternative formulation for estimating the GMM parameters using the sliced Wasserstein distance, which gives rise to a new algorithm. Specifically, we propose minimizing the sliced-Wasserstein distance between the mixture model and the data distribution with respect to the GMM parameters. In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters. We show that our formulation results in parameter estimates that are more robust to random initializations and demonstrate that it can estimate high-dimensional data distributions more faithfully than the EM algorithm

Pre-processing Technique for Wireless Capsule Endoscopy Image Enhancement

Wireless capsule endoscopy (WCE) is used to examine human digestive tract in order to detect abnormal area. However, it has been a challenging task to detect abnormal area such as bleeding due to poor quality and dark images of WCE. In this paper, pre-processing technique is introduced to ease classification of the bleeding area. Anisotropic contrast diffusion method is employed in our pre-processing technique as a contrast enhancement of the images. There is a drawback to the method proposed B. Li in which the quality of WCE image is degraded when the number of iteration increases. To solve this problem, variance is employed in our proposed method. To further enhance WCE image, Discrete Cosine Transform is used with anisotropic contrast diffusion. Experimental results show that both proposed contrast enhancement algorithm and sharpening WCE image algorithm provide better performance compared with B. Li’s algorithm since SDME and EBCM value is stable whenever number of iterations increases, and sharpness measurement using gradient and PSNR are both improved by 31.5% and 20.3% respectively

Gaussian mixture model-based contrast enhancement

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method

Multimodal enhancement-fusion technique for natural images.

Masters Degree. University of KwaZulu-Natal, Durban.This dissertation presents a multimodal enhancement-fusion (MEF) technique for natural images. The MEF is expected to contribute value to machine vision applications and personal image collections for the human user. Image enhancement techniques and the metrics that are used to assess their performance are prolific, and each is usually optimised for a specific objective. The MEF proposes a framework that adaptively fuses multiple enhancement objectives into a seamless pipeline. Given a segmented input image and a set of enhancement methods, the MEF applies all the enhancers to the image in parallel. The most appropriate enhancement in each image segment is identified, and finally, the differentially enhanced segments are seamlessly fused. To begin with, this dissertation studies targeted contrast enhancement methods and performance metrics that can be utilised in the proposed MEF. It addresses a selection of objective assessment metrics for contrast-enhanced images and determines their relationship with the subjective assessment of human visual systems. This is to identify which objective metrics best approximate human assessment and may therefore be used as an effective replacement for tedious human assessment surveys. A subsequent human visual assessment survey is conducted on the same dataset to ascertain image quality as perceived by a human observer. The interrelated concepts of naturalness and detail were found to be key motivators of human visual assessment. Findings show that when assessing the quality or accuracy of these methods, no single quantitative metric correlates well with human perception of naturalness and detail, however, a combination of two or more metrics may be used to approximate the complex human visual response. Thereafter, this dissertation proposes the multimodal enhancer that adaptively selects the optimal enhancer for each image segment. MEF focusses on improving chromatic irregularities such as poor contrast distribution. It deploys a concurrent enhancement pathway that subjects an image to multiple image enhancers in parallel, followed by a fusion algorithm that creates a composite image that combines the strengths of each enhancement path. The study develops a framework for parallel image enhancement, followed by parallel image assessment and selection, leading to final merging of selected regions from the enhanced set. The output combines desirable attributes from each enhancement pathway to produce a result that is superior to each path taken alone. The study showed that the proposed MEF technique performs well for most image types. MEF is subjectively favourable to a human panel and achieves better performance for objective image quality assessment compared to other enhancement methods

On Box-Cox Transformation for Image Normality and Pattern Classification

A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda ({\lambda}) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assumption underlies a variety of statistical test models. This technique, however, is best known in statistical analysis to handle one-dimensional data. Herein, this paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data, namely, digital images and to study its effect. Moreover, to reduce time complexity, it suffices to estimate the parameter lambda in real-time for large two-dimensional matrices by merely considering their probability density function as a statistical inference of the underlying data distribution. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images and for ameliorating the performance of a colour pattern classification algorithm as an example application. Results with and without the proposed approach, are compared using the AlexNet (transfer deep learning) pretrained model. To the best of our knowledge, this is the first time that the Box-Cox transformation is extended to digital images by exploiting histogram transformation.Comment: The paper has 4 Tables and 6 Figure

Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery

医疗机器人手术视觉是微创外科手术成功与否的关键所在。由于手术器械医学电子内镜自身内在的局限性，导致了手术视野不清晰、光照不均、多烟雾等诸多问题，使得外科医生无法准确快速感知与识别人体内部器官中的神经血管以及病灶位置等结构信息，这无疑增加了手术风险和手术时间。针对这些手术视觉问题，本论文提出了一种基于双边滤波权重分析的多尺度Retinex模型方法，对达芬奇医疗机器人手术过程中所采集到的病患视频进行处理与分析。经过外科医生对实验结果的主观评价，一致认为该方法能够大幅度地增强手术视野质量；同时客观评价实验结果表明本论文所提出方法优于目前计算机视觉领域内的图像增强与恢复方法。 厦门大学信息科学与技术学院计算机科学系罗雄彪教授为本文第一作者。【Abstract】Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in-situ vision is paramount not only for safety by preventing inadvertent injury, but also to improve precision and reduce operating time. Unfortunately, endoscopic vision is unavoidably degraded due to illumination variations during surgery. This work aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective noreference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate endoscopic visualization on surgical video sequences. The methods were validated on surgical data, with the experimental results showing that our method outperforms existent retinex approaches. In particular, the combined visibility was improved from 0.81 to 1.06, while three surgeons generally agreed that the results were restored with much better visibility.The authors thank the assistance of Dr. Stephen Pautler for facilitating the data acquisition, Dr. A. Jonathan McLeod and Dr.Uditha Jayarathne for helpful discussions