In this section, we implemented seven histogrambasedtechniques for digital image enhancement and performance metrics AMBE, PSNR and Entropy are calculated. These techniques are HE, BBHE, RMSHE, RSIHE, MMBEBHE, MPHEBP and BPDHE. AMBE is used to assess the degree of brightness preservation while both PSNR and Entropy are employed to quantitatively assess the degree of contrast enhancement. In addition, for the qualitative assessment of contrast enhancement, we visually inspect the output image. Simulation of various Histogram Equalization techniques, were performed using Mat-lab v7.7 software. Enhancement techniques are applied on the images of different sizes and from different application fields like real images, medical images etc.
into account. The experimental results show that the proposed method Spatially weighted histogram has better performance than the existing methods, and preserve the original brightness quite well, so that it is possible to be utilized in consumer electronic products. To reduce undesired artifacts associated with the conventional histogram, a weight function according to each pixel’s spatial activity is introduced to make the contrast enhancement appropriate to the human observers. Then the grey scale transform function is calculated by accumulating this spatially weighted histogram. Finally, the transform function is modified to make sure that the mean output intensity will be almost equal to the mean input intensity. Here the proposed method had achieved visually more pleasing contrast enhancement while maintaining the input brightness. More importantly, the amount of calculation and storage involved in this algorithm is rather low which makes it more competitive in real-time processing . Ravichandran and Magudeeswaran (2012) proposed mean brightness preserving Histogram Equalization basedtechniques for image enhancement. Generally, these methods partition the histogram of the original image into sub histograms and then independently equalize each sub- histogram with Histogram Equalization. The comparison of recent histogrambasedtechniques is presented for contrast enhancement in low illumination environment and the experiment results are collected using low light environment images. The histogram modification algorithm is simple and computationally effective that makes it easy to implement and use in real time systems . Vinod Kumar (2012), here author presents about Contrast enhancement of digital images is conveniently achieved by spreading out intensity
Abstract: The Image analysis is difficult in the Magnetic Resonance Imaging(MRI) which are having poor image quality. Especially the bygone, artifacts inherent images and low contrast between tissues and inter individual variability makes affect on the accuracy of clinical diagnosis. Consequently, So we need image enhancement techniques to improve the relevant image contents while preserving the actual details features. This paper presents comparative study of the most commonly used Histogram-basedtechniques, Mainly Histogram equalization(HE),Adaptive Histogram Equalization(AHE),Contrast Limited Adaptive Histogram equalization(CLAHE) and Brightness Preserving Dynamic Fuzzy Histogram equalization(BPDFHE) techniques, Dealing with contrast enhancement of MRI images.
This paper reviews and highlights the milestone researches in energy aware mobile phone display and GUI design. This study surveys the energy saving methods of display, such as display backlight control, OLED, AMOLED power reduction strategies and histogrambasedtechniques. Among the strategies of OLED power reduction, Mion Dong et al achieved high energy savings result of 75 % using the transformation of energy aware color set and various color mapping methods. AMOLED displays are more energy efficient than the other displays, Xiang Chen et al reveals that sports videos occupies more power than other videos in AMOLED displays. Pi-Cheng Hsiu et al achieved maximum of 31% of energy saving by using dynamic programming algorithm to reduce backlight level.
Abstract - Image Enhancement is a vast area of Image Processing with its applications in different areas. Image Enhancement is used to transform digital images to enrich the visual information inside it. It is an initial operation for almost all vision and image processing assignment in several areas such as computer vision, biomedical image Processing, forensic image analysis, remote sensing and fault detection. In image enhancement certain transformations are applied upon an input image to obtain a visually more acceptable, more comprehensive and less noisy output image. In this paper four histogram equalization based Image Enhancement Techniques, CHE (Conventional Histogram Equalization), BBHE (Brightness Preserving Bi-Histogram Equalization), DSIHE (Dual Sub-Image Histogram Equalization) and MMBEBHE (Minimum Mean Brightness Error Bi-HE), are compared. All these techniques are based on partitioning of histogram of image and then equalizing each part separately. These techniques are assessed qualitatively and after examining output image visually, we see if it retains an appearance which is perfectly natural.
Image enhancement is one of the challenging issues in low level image processing. Contrast enhancement techniques are used for improving visual quality of low contrast images. Histogram Equalization (HE) method is one such technique used for contrast enhancement. In this paper, various techniques of image enhancement through histogram equalization are overviewed. To evaluate the effectiveness of the methods illustrated, we have used the PSNR, tenengrad, and contrast as parameters. These parameters show that how the results vary on applying different techniques of enhancement.
Nowadays, requirement of cameras gets increased in applications such as intelligent surveillance systems to monitor the public and private sections. In Intelligent Transport Systems, cameras monitor the situation about traffic, vehicle crashes and accidents and they record each and every minute. But poor visibility because of the weather or poor atmospheric light will lead to the Intelligent systems to give poor performance. There are many image processing techniques to overcome this issue such as illumination adjustment, scaling, de noising etc. In ITS pre-processing the image at high speed is necessary. In this paper we focus on image enhancement using CLAHE algorithm technique.
For colour images with RGB representation, the colour of a pixel is a mixture of the three primitive colours red, green, and blue. RGB is suitable for colour display, but not good for colour scene segmentation and analysis because of the high correlation among the R, G, and B components [5, 26]. By high correlation, we mean that if the intensity changes, all the three components will change accordingly. In this context, colour image segmentation using evidence theory appears to be an interesting method. However, to fuse diﬀerent images using DS theory, the appropriate determination of mass function plays a crucial role, since assignation of a pixel to a cluster is given directly by the estimated mass functions. In the present study, the method of generating the mass functions is based on the assumption of a Gaussian distribution. To do this, histogram analysis is applied simultaneously to both the homogeneity and the colour feature domains. These are used to extract homogeneous regions in each primitive colour. Once the mass functions are estimated, the DS combination rule is applied to obtain the final segmentation results.
914 | P a g e change the marker of tracking but it holds the control over the object which might be useful for security purposes. It is very difficult to resolve the occluding situation in the surveillance system. This is because of camera is missing the occluded entity. The information needed to track the occluded object remains unavailable during an occlusion. Tracking application faces the occlusion problem in different ways. But none of these ways are stand alone to resolve problem if system uses single camera. System with multiple camera arrangement can remove the problem successfully. As our system is based on single camera, the problem is defended by tracking the occluded entity as a whole.
histogram equalization(CLAHE) is contrast limiting. The CLAHE produces clipping limit for histogram to overcome the noise amplification problem. The CLAHE method divides the image in to relative regions and applies the histogram equalization process to each region. CLAHE has two parameters clip limit(CL) and block size which are mainly control image enhancement quality. By increasing the clip limit the image brightness will be increased. Similarly by increasing block size the range becomes larger due to these the image contrast also increases. CLAHE is one of the most widely and established method for the successful enhancement of low-contrast images. The CLAHE method consists the following 7 steps 1) Dividing the original intensity image into non-
Auscultation is quite reliable technique at the same time quite difficult to master. With the stethoscope, it requires highly experienced professionals to read and understand phonocardiograph. Content-based indexing of audio (and multimedia) data has become more important since conventional databases cannot provide the necessary efficiency and performance [1, 2]. However, there are few main difficult problems. First, the content of audio data is subjective information; it is hard to give the descriptions in words. The recognition of data content requires prior knowledge and special techniques in Signal Processing and Pattern Recognition, which usually require long computing time. Second, since several audio features can be used as indices  (such as pitch, amplitude, and frequency), a method or processing technique designed and developed for one feature may not be appropriate for another. Thus, there arises the necessity of content based analysis of phonocardiography (PCG) signal. In this paper we propose a PCG retrieval system, based on modular architecture consisting of four stages. Feature extraction, Histogram modeling, Pattern Matching using different techniques and retrieval.
Figueiredo et al proposal expected to introduce a variation image segmentation method for assess the aberrant crypt foci (ACF) in the person colon captured in vivo by endoscopy. The proposed segmentation technique enhanced the active contours without edges model of Chan and Vese to account forthe ACF's particular structure. Level sets to represent the segmentation boundaries and discretize in space by finite elements and in (artificial) time by fixed difference sare employed. Yazdanpanah et al  presented a semi-automated segmentation algorithm to detect intra-retinal layers inOCT images acquired from rodent models of retinal degeneration. The proposed segmentation technique was adapted Chan-Vese's energy-minimizing active contours without edges for the OCT images, which inturn suffered from low contrast and were highly tarnished by noise. Further Dana et. al.  the adaptive integration of the colour and texture attributes in the development of complex image descriptors is highlighted. The substantial interest shown by the research community in colour–texture-based segmentation is mainly motivated by two factors. Delu Zeng et al considered the task of object segmentation and achieve in a novel manner that backed by the Poincar map method in a defined vector field in view of dynamical systems. n interpolated swirl and attract flow ( F) vector field is first generated for the observed image. Then, the states on the limit cycles. In Nguyen, et. al  proposed a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms.
Image enhancement is aprocess of changing the pixels intensity of the input image;to make the output image subjectively look better. Contrast enhancement is an important area in image processing for both human and computer vision. It is widely used for medical image processing and as a pre-processing step in speech recognition, texture synthesis, and many other image/video processing applications. Contrast enhancement plays a crucial role in image processing applications, such as digital photography, medical image analysis, remote sensing, LCD display processing, and scientific visualization. There are several reasons for an image/ video to have poor contrast: the poor quality of the used imaging device, lack of expertise of the operator and the adverse external conditions at the time of acquisition. These effects result in under-utilization of the offered dynamic range. As a result, such images and videos may not reveal all the details in the captured scene and may have a washed out and unnatural look. Contrast enhancement targets to eliminate these problems, thereby to obtain a mor visually-pleasing or informative image or both. Histogram equalization is a well-known contrast enhancement technique due to its performance on almost all types of image. Contrast is created by the difference in luminance reflectance from two adjacent surfaces. In our visual perception, contrast is determined by the difference in the color and brightness of an object with other objects. If the contrast of an image is highly concentrated on a specific range, the information may be lost in those areas which are excessively and uniformly concentrated. The problem is to enhance the contrast of an image in order to represent all the information in the input image.Brightness preserving methods are in very high demand to the consumer electronic products. Numerous histogram equalization (HE) based brightness preserving methods tend to produce unwanted artefacts.
Even smart intruders must apply several Steganalysis attacks to destroy the secret or require exact extraction mechanism to excerpt the secret. In this genre, another technique comes in place called digital watermarking, where a secret is hidden to prove the authenticity of the carrier. Although the technique is similar with respect to data hiding, purpose of the two are quite different. The main objective of steganography is imperceptibility or undetectability which means no perceptual degradation in Stego-object. In other words, there should not be any clue of data hiding in the Stego-object. The next important objective is robustness. Robustness means ability to withstand adverse situations which encourages the evolution of techniques which cannot be tampered through Steganalysis attacks. Steganography can be blind or non-blind and key based or keyless. Blind steganography doesn’t require original cover to extract the embedded secret whereas non-blind technique does. Key based steganography uses a key to embed and extract secret data. These keys even shared with recipient for successful extraction.
Structural Similarity Index (SSIM) is a method to determine the quality of a digital image or video, developed at the Laboratory for Image and Video Engineering (LIVE) at the University of Texas, Austin in collaboration with New York University. Developed by Zhou Wang et al in a paper entitled Image quality assessment: From error visibility to structural similarity . SSIM is used to measure the similarity between the two images. SSIM index is a full reference matric, in other words the measurement or prediction based on the image quality of the initial image (the image that has not been compressed / image that is free from noise) as a reference. SSIM made to improve existing methods such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE) which that both methods proved to be inconsistent compare with human visual perception.
CBIR system focuses on retrieving images from the database; the system depends on the way the indexing is being implemented. The way or method in which an image is stored will affect how it will be retrieved later and which can save more storage space and improve the retrieval process. Building effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively.
In last few years with the development of computer technology and network communication technology, specially the emergence and popularization of Internet, and social media like facebook ,Instagram and etc, rapid expansion of the size of the multimedia database such as digital image, every day hundreds of millions of military or civilian image data is stored in the database. The main method of image files is to establish Keywords or text description of the title as well as some additional information, and then establish a link between storage path and the keywords of the image, which is text-based image retrieval. However, with the storage capacity of images to start using GB or TB, the own shortcomings of text-based image retrieval technology led to two difficulties in the retrieval: First, it has been impossible to note each image; second, the subjectivity and non-precision of image annota- tion may lead to the adaptation in the retrieval process. In order to overcome these problems, Content-Based Image Retrieval (CBIR) was proposed in the 1990’s .Contents Based Image Retrieval (CBIR) is a very important and prominent area in image processing due to its diverse applications in internet, multimedia, companies image archives and medical image archives. Improved demand for image databases has increased the need to store and retrieve digital images. And the most important features or image component that used in Content Based Image Retrieval (CBIR) are texture, color and shape [1, 2].
Satellite image compression involves many factors should be taken in to consideration, like Compression ratio (bpp), PSNR, memory requirements and computational time. In  G.K.Wallace explained JPEG compression standard which was very ubiquitous for compression standards.JPEG standard is based on DCT (Discrete Cosine Transform) and is having advantages of real transform, low memory requirement. The JPEG methods have limitations like blocking artifact at low bitrates, Huffman table requirement for low bitrates. The method JPEG 2000 is based on wavelet, this method offers better PSNR at low bit rates, this method has limitations like ringing effect and low resolution. In  Khaled sahnoun performed the satellite image compression based on the FFT, the method is computationally complex and less significant in energy compaction. In  it was explained that the histogram influences threshold selection and the better thresholds based on Otsu method will reduce intra and inter class variances. In  sujoys approach of image compression based on multilevel thresholding and the entropy was maximized using differential evolution,this method is appealing interms of entropy improvement at the same time with a limitation of computational time. In  J N kapur worked on gray-level picture thresholding using the entropy of the histogram. In  and  it was explored various quantization techniques for compression and signal processing. Land sat images  are of sizes 7811 x 7641,16bit length with the intensity variation of 0-65535. The Landsat 8 has 11 bands of data for each location and the amount of storage required will be in the order of 2GB.So memory requirements are thirsty for satellite images.
HE procedures are broadly used in our quotidian life, such that in the field of Medical picture transforming, shopper gadgets, picture matching and testing, verbalization perception and surface blend on the grounds that it has high productivity and straightforwardness. The best well known strategy for differentiation upgrade of pictures is histogram balance (HE) [7-11]. It is one of the decently kenned techniques for upgrading the complexity of a given picture as per the examples circulation [12, 13]. HE is a basic and effective complexity improvement strategy which conveys pixel values consistently such that upgraded picture have direct aggregate histograms. It develops the difference of the high histogram districts and clamps the differentiation of the low histogram areas . The HE procedure is a worldwide operation, consequently; it doesn't save the picture luminosity. HE has been generally connected when the picture needs an upgrade, for example, radar picture preparing, therapeutic picture handling, surface amalgamation, and verbalization perception [10, 15-16]. HE routinely brings two sorts of relics into the balanced picture to be specific over-improvement of the picture districts with more successive light black levels, and the loss of differentiation in the picture areas with less continuous ash levels . To surmount these disadvantages a few HE-predicated procedures are proposed and are more focused on the safeguarding of picture brightness than the enhancement of picture complexity. Few techniques frequently cause pictures with angering visual ancient rarities and unnatural appearances; however the picture brilliance is safeguarded to some degree .
Content-Based Image Retrieval (CBIR) systems are search engines for image databases, which index images according to their content. Content-based image retrieval is the application of computer vision techniques to the problem of digital image search in large databases. During the last several years; CBIR emerged as powerful tool to efficiently retrieve images visually similar to query image. This paper presents image retrieval using Color Histogram Method (CHM). The primary goal of the proposed method is to improve the matching efficiency. The process of image retrieval using CHM involves steps like selection of the color space, quantization of the color space, extraction of the color feature using histogram, finding the distance between the query and test images using an appropriate distance function, sorting the retrieved distance values and finally ranking the retrieved images. In general, the color histogram method using quadratic distance metric showed good performance of image retrieval in considering both computation time and retrieval effectiveness with high precision values with good speed. Of course, this paper does not mark the end of research in the domain of content based image retrieval. To further improve the retrieval results, segmentation may be used to extract regions and objects from the image.