Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image

Fusion CLAHE-based image enhancement with fuzzy set theory on field images

In this paper, a new fusion of Contrast-Limited Adaptive Histogram Equalisation or CLAHE-based method is proposed to enhance field images. The field images, which are low resolution images, were taken using a camera or other devices such as smartphones with lower quality as compared to the lab images with proper setup. The field images had low contrast and were blurred and unsharp due to inconsistent setting or environment exposures. Image enhancement helps to enrich the perception of images for better quality, reduce impulsive noise, and sharpen the edges with the help of different image enhancement techniques. The main attraction towards the enhancement of this research area is due to the additional knowledge and hidden information provided by the results of this procedure, which will further be used for many different useful purposes. This research proposes a fusion of CLAHE-based with Fuzzy set theory. An optimisation technique was applied to increase the enhancement ratio. The result of the proposed fusion method was compared with the standard method as a benchmark. The obtained value is compared by using image quality measurement techniques. The proposed fusion method produces better quality and enhanced images and required minimum processing time than the other methods

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

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

Adaptive Filters for 2-D and 3-D Digital Images Processing

Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideálními obrazy obsahujícími aditivní a impulzní šum. Závěr práce se věnuje prostorové rekonstrukci objektů na základě jejich optických řezů. Veškeré postupy a algoritmy jsou i prakticky zpracovány v softwaru, který byl vyvinut v rámci této práce.The thesis is concerned with filters for visualization of high dynamic range images. In the theoretical part, the principle of confocal microscopy is described and the term digital image is defined in a mathematically correct way. Both frequency approach (using 2-D and 3-D discrete Fourier transform and frequency filters) and digital geometry approach (using adaptive histogram equalization with adaptive neighbourhood) are chosen for the processing of images. Necessary adjustments when working with non-ideal images containing additive and impulse noise are described as well. The last part of the thesis is interested in 3-D reconstruction from optical cuts of an object. All the procedures and algorithms are also implemented in the software developed as a part of this thesis.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083