An Improved Approach for Contrast Enhancement of Spinal Cord Images based on Multiscale Retinex Algorithm

This paper presents a new approach for contrast enhancement of spinal cord medical images based on multirate scheme incorporated into multiscale retinex algorithm. The proposed work here uses HSV color space, since HSV color space separates color details from intensity. The enhancement of medical image is achieved by down sampling the original image into five versions, namely, tiny, small, medium, fine, and normal scale. This is due to the fact that the each versions of the image when independently enhanced and reconstructed results in enormous improvement in the visual quality. Further, the contrast stretching and MultiScale Retinex (MSR) techniques are exploited in order to enhance each of the scaled version of the image. Finally, the enhanced image is obtained by combining each of these scales in an efficient way to obtain the composite enhanced image. The efficiency of the proposed algorithm is validated by using a wavelet energy metric in the wavelet domain. Reconstructed image using proposed method highlights the details (edges and tissues), reduces image noise (Gaussian and Speckle) and improves the overall contrast. The proposed algorithm also enhances sharp edges of the tissue surrounding the spinal cord regions which is useful for diagnosis of spinal cord lesions. Elaborated experiments are conducted on several medical images and results presented show that the enhanced medical pictures are of good quality and is found to be better compared with other researcher methods.Comment: 13 pages, 6 figures, International Journal of Imaging and Robotics. arXiv admin note: text overlap with arXiv:1406.571

Improvement of Underwater Image Contrast Enhancement Technique Based on Histogram Modification

Degradasi kontras adalah salah satu masalah imej bawah air yang mengakibatkan pengurangan keamatan cahaya. Kontras yang rendah menyumbang kepada masalah imej yang mempunyai kurang maklumat. Objek dalam imej dilihat tidak jelas. Tambahan juga, penyerapan cahaya menyebabkan imej yang diambil kelihatan berwarna biru-kehijauan seterusnya warna objek akan disalah tafsir. Selain itu, kewujudan kawasan yang gelap dan terlalu cerah menyebabkan pengurangan keperincian imej. Oleh itu, untuk mengurangkan masalah yang dinyatakan di atas, tiga teknik untuk meningkatkan kontras imej di bawah air telah dicadangkan dalam kajian ini, iaitu model warna bersepadu dengan pengagihan Rayleigh (ICM-RD), Rayleigh-regangan dan purata paksi imej (RSAIP), dan regangan- Rayleigh dua imej spesifikasi histogram penyesuaian terhad (DIRS-CLAHS). ICM-RD meningkatkan kontras imej di bawah air dengan mengintegrasikan pengagihan Rayleigh dalam proses regangan yang terhad. Seterusnya, pembetulan warna imej melalui model warna Hue-Ketepuan-Nilai (HSV) memperbaiki keseluruhan warna imej. Di samping itu, kaedah RSAIP dicadangkan bagi menyelesaikan masalah had regangan bagi proses regangan yang dihadapi oleh kaedah ICM-RD. Kaedah RSAIP menyediakan satu alternatif baharu bagi proses regangan, yang mana imej histogram akan dibahagi kepada dua bahagian dan diregangkan secara berasingan bagi memenuhi ruang dinamik imej yang ditetapkan. Proses pembahagian dan regangan ini menghasilkan dua imej yang berbeza keamatan. Kedua-dua imej yang dihasilkan akan digabungkan berdasarkan nilai purata dan diaplikasikan dengan kaedah pembetulan warna bagi menghasilkan imej akhir. Kaedah yang ketiga, DIRSCLAHS, dicadangkan bagi meningkatkan keupayaan kaedah RSAIP dalam mempertingkatkan kontras imej dengan mengintegrasikan pembetulan kontras global dan tempatan. Proses DIRS-CLAHS bermula dengan pembetulan kontras global yang diperkenalkan dalam kaedah RSAIP. Pembetulan kontras tempatan dilaksanakan dengan membahagikan imej kepada bahagian yang lebih kecil. Akhirnya, proses ini diaplikasikan dengan proses pembetulan warna yang merupakan modifikasi daripada proses pembetulan warna yang diperkenalkan dalam kaedah RSAIP dan ICM-RD. Secara prinsipnya, semua teknik yang dicadangkan mengatasi kualiti teknik terbaharu yang diperkenalkan secara kualiti dan kuantiti. Daripada tiga teknik yang dicadangkan, kaedah DIRS-CLAHS menunjukkan satu peningkatan yang baik dalam meningkatkan kontras imej bawah air dan warnanya. Secara kuantiti, perbandingan dengan enam teknik terbaharu yang diperkenalkan bagi 300 sampel imej, kaedah DIRS-CLAHS menghasilkan nilai purata entropi yang tertinggi iaitu 7.624 dan nilai purata MSE yang terendah iaitu 646.32. Malah, dari segi pengukuran peningkatan (EME) dan pengukuran peningkatan berdasarkan entropi (EMEE), DIRSCLAHS menghasilkan nilai purata tertinggi iaitu masing-masing 27.096 dan 9.670. ________________________________________________________________________________________________________________________ Contrast degradation is one of the problems of underwater image that resulted from the light attenuation. Low contrast contributes towards the less usable image where less information could be extracted from the image. The objects seen in the image are unclear. In addition, light absorption phenomenon causes the underwater image to be dominant by the blue-green illumination, resulting in misinterpretation of objects color. Therefore, to reduce the aforementioned problems of underwater image and increases underwater image contrast, three techniques of improving underwater image contrast are proposed in this study, namely integrated color model with Rayleigh distribution (ICM-RD), Rayleigh-stretching and averaging image planes (RSAIP), and dual-images Rayleigh-stretched contrast limited adaptive histogram specification (DIRS-CLAHS). ICM-RD improves the underwater image contrast by integrating the Rayleigh distribution in the limited stretching process. The correction of image color through Hue-Saturation-Value (HSV) color model further improves the overall image color. On the other hand, RSAIP method solves the limitation of stretching process that faced by ICM-RD method. The RSAIP method provides an alternative stretching technique, where the histogram of the original image is divided into two independent regions and stretched independently to occupy the limited dynamic intensity range. The dividing and stretching processes produce two different intensity images. These images are then combined by means of average value and applied with color correction technique to produce final resultant image. The third proposed method, DIRS-CLAHS method is designed to improve the capability of the RSAIP method in enhancing image contrast by integrating global and local contrast correction. DIRS-CLAHS is first applied with global contrast correction which is introduced in the RSAIP method. Local contrast correction is then applied by dividing the image into smaller tiles. Finally, the method is applied with a new color correction process which is a modification of color correction process introduced in RSAIP and ICM-RD methods. All proposed techniques, principally outperform the state-of-the-art methods, qualitative and quantitatively. Out of the three proposed methods, DIRS-CLAHS method, is the best method and demonstrates a significant enhancement in improving the underwater image contrast and its color. Quantitatively, in comparison with six state-of-the-art methods for 300 samples of underwater images, the proposed DIRS-CLAHS produces the highest average entropy of 7.624 and the lowest average MSE value of 646.32. In addition, in terms of measure of enhancement (EME) and measure of enhancement by entropy (EMEE), DIRSCLAHS produces the highest average values which are 27.096 and 9.670, respectively

LWT-CLAHE Based Color Image Enhancement Technique: An Improved Design

Color image enhancement is one of important process and actually a vital precursory stage to other stages in the field of digital image processing. This is due to the fact that the effectiveness of processes in this stage on the output determines the success of other stages for a quality overall performance. This paper presents a color image enhancement technique using lifting wavelet transform (LWT) and contrast limited adaptive histogram equalization (CLAHE) to overcome the issue of noise amplification, over and under-enhancement in exiting enhancement techniques. Test images from Computer Vision Database were used for the proposed technique and the performance was evaluated using PSNR and SSIM. Result obtained shows an average improvement of 56.4% and 20.98% in terms of PSNR and SSIM respectively

Review on Efficient Contrast Enhancement Technique for Low Illumination Color Images

A digital color image, as its fundamental purpose requires, is to provide a perception of the scene to a human viewer or a computer for carrying out automation tasks such as object recognition. An image of high quality that could truly represent the captured object and the scene is hence in great demand.Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other object and background. On the contrary, the human visual perception is interested in hue (H), saturation (S) and intensity (I) attributes that are carried by the color image. Therefore, when the image has to be processed, most approaches convert the RGB space into some convenient working signal spaces that are close to human perceptions

Contrast enhancement and exposure correction using a structure-aware distribution fitting

Realce de contraste e correção de exposição são úteis em aplicações domésticas e técnicas, no segundo caso como uma etapa de pré-processamento para outras técnicas ou para ajudar a observação humana. Frequentemente, uma transformação localmente adaptativa é mais adequada para a tarefa do que uma transformação global. Por exemplo, objetos e regiões podem ter níveis de iluminação muito diferentes, fenômenos físicos podem comprometer o contraste em algumas regiões mas não em outras, ou pode ser desejável ter alta visibilidade de detalhes em todas as partes da imagem. Para esses casos, métodos de realce de imagem locais são preferíveis. Embora existam muitos métodos de realce de contraste e correção de exposição disponíveis na literatura, não há uma solução definitiva que forneça um resultado satisfatório em todas as situações, e novos métodos surgem a cada ano. Em especial, os métodos tradicionais baseados em equalização adaptativa de histograma sofrem dos efeitos checkerboard e staircase e de excesso de realce. Esta dissertação propõe um método para realce de contraste e correção de exposição em imagens chamado Structure-Aware Distribution Stretching (SADS). O método ajusta regionalmente à imagem um modelo paramétrico de distribuição de probabilidade, respeitando a estrutura da imagem e as bordas entre as regiões. Isso é feito usando versões regionais das expressões clássicas de estimativa dos parâmetros da distribuição, que são obtidas substituindo a mé- dia amostral presente nas expressões originais por um filtro de suavização que preserva as bordas. Após ajustar a distribuição, a função de distribuição acumulada (CDF) do modelo ajustado e a inversa da CDF da distribuição desejada são aplicadas. Uma heurística ciente de estrutura que detecta regiões suaves é proposta e usada para atenuar as transformações em regiões planas. SADS foi comparado a outros métodos da literatura usando métricas objetivas de avaliação de qualidade de imagem (IQA) sem referência e com referência completa nas tarefas de realce de contraste e correção de exposição simultâneos e na tarefa de defogging/dehazing. Os experimentos indicam um desempenho geral superior do SADS em relação aos métodos comparados para os conjuntos de imagens usados, de acordo com as métricas IQA adotadas.Contrast enhancement and exposure correction are useful in domestic and technical applications, the latter as a preprocessing step for other techniques or for aiding human observation. Often, a locally adaptive transformation is more suitable for the task than a global transformation. For example, objects and regions may have very different levels of illumination, physical phenomena may compromise the contrast at some regions but not at others, or it may be desired to have high visibility of details in all parts of the image. For such cases, local image enhancement methods are preferable. Although there are many contrast enhancement and exposure correction methods available in the literature, there is no definitive solution that provides a satisfactory result in all situations, and new methods emerge each year. In special, traditional adaptive histogram equalization-based methods suffer from checkerboard and staircase effects and from over enhancement. This dissertation proposes a method for contrast enhancement and exposure correction in images named Structure-Aware Distribution Stretching (SADS). The method fits a parametric model of probability distribution to the image regionally while respecting the image structure and edges between regions. This is done using regional versions of the classical expressions for estimating the parameters of the distribution, which are obtained by replacing the sample mean present in the original expressions by an edge-preserving smoothing filter. After fitting the distribution, the cumulative distribution function (CDF) of the adjusted model and the inverse of the CDF of the desired distribution are applied. A structure-aware heuristic to indicate smooth regions is proposed and used to attenuate the transformations in flat regions. SADS was compared with other methods from the literature using objective no-reference and full-reference image quality assessment (IQA) metrics in the tasks of simultaneous contrast enhancement and exposure correction and in the task of defogging/dehazing. The experiments indicate a superior overall performance of SADS with respect to the compared methods for the image sets used, according to the IQA metrics adopted

Developing a New Algorithm to Detect Right Thumb Fingernail in Healthy Human

Due to significant challenges faced by traditional methods of personal identification like fingerprinting, eye scanning, and voice recognition, new techniques are needed. One such approach involves the use of human nail images for identification and access to personal identification programs and electronic patient files. A novel algorithm, which consists of three stages, has been proposed utilizing the HSV color space detection algorithm, grayscale contrast optimization algorithm, nail segmentation, and image smoothing with a Gaussian filter. This method reduces tested image data and preserves the primary image structure, and has the potential to surpass the accuracy of traditional methods, providing an additional layer of security in personal identification programs and electronic patient files. Nail image detection can be conducted remotely and accessed through standard cameras or smartphones, making it a more hygienic and convenient option than physical contact methods such as fingerprinting or eye scanning. Moreover, the use of nail images for personal identification has several other benefits, especially in situations where traditional methods are not feasible, such as in individuals with skin conditions that prevent fingerprinting. The success of the proposed algorithm in detecting nail images for personal identification has implications beyond individual security and can be applied in different fields, including healthcare and forensic science, to improve identification accuracy and prevent fraud. For example, the use of nail images could help prevent identity theft in healthcare settings, where sensitive information is stored and exchanged

Image enhancement for underwater mining applications

The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this thesis work, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior (DCP) and then taking the converted images and modifying them into the Long, Medium and Short (LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at LSA (Laboratório de Sistema Autónomos) robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. This thesis work describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation