A Comparative Analysis on Image Quality Assessment for Real Time Satellite Images

Abstract: Radiography is one of the important clinical adjuncts for preliminary disease investigation. The X-ray images are corrupted with inherent quantum noise affecting the performance of computer-aided diagnosis systems. This paper presents an extensive experimental review and impact of six benchmark filters for reducing noise and disease classification on chest X-ray images. The tradeoff between de-noising and texture preserving performance is investigated through classification performances using the state-of-the-art machine learning methods – Support Vector Machine and Artificial Neural Network. Moreover, the qualitative, subjective, and statistical evaluation is performed by using the image quality metrics, expert radiologist opinion, and statistical test, respectively. The experimental results confirm the significant improvement in classification performance using Guided filtered images. Furthermore, the results of qualitative measures and subjective analysis demonstrate that the guided filter and anisotropic diffusion filter both performed significantly better. Finally, a non-parametric statistical test is used to validate statistical significance of the obtained results.

Abstract: We present a novel technique to examine the consequence of cataract surgery. Iris is one of the best biometric due to its unique biological properties. It is stable throughout the life. Cataract surgery may cause changes in the iris structure. A cataract is an eye disease that causes the eye’s lens to become cloudy and opaque with decreased vision. Aging, obesity, family history, heavy drinking, high blood pressure, smoking, diabetes, myopia or sun exposure are the risk factors of cataract. This paper proposes an algorithm to improve the level of security or match the unmatched person due to structural and textural changes in irises. Feature vector results from gober are applied to SOM clustering approach. Our proposed method has been evaluated by a dataset composed of different irises of an individual before and after the surgery which are collected from various studies. Experimental results and performance comparisons have presented.

Abstract: The major problems of Visual Secret Sharing (VSS) are the pixel expansion and lossy recovery. The former creates large-sized shared images and makes their handling, storage, and speed transmission via networks challenging, whereas the latter leads to poor contrast of the recovered images. In addition, sharing a huge volume of images and transmitting the shared images through one less channel is a critical problem of VSS where any unauthenticated user can attack, discover the generated shares, and recover the secret image. In this paper, an efficient TKHC algorithm is proposed to augment the privacy and safety of the shared images. Moreover, the new TKHC-based VSS scheme is utilized to sharing a huge RGB and grayscale images which are subjected to be encrypted and decrypted by means of TKHC and providing strong security to transmit all the generated shares via one public channel. In comparison to the existing schemes, the proposed scheme shows significant improvement in encryption quality with lightweight computation cost. Furthermore, it withstands the known-plaintext and brute-force attacks and overall creates a balance between security, cost, and performance.

Abstract: The age of digital information uses images in fields like military and medical applications, but the security of those transacted images is still a question mark. To overcome this challenge, an efficient encryption system has to be developed which should accomplish confidentiality, integrity, security and it should also prevent the access of images by unauthorized users. Such an image encryption system has to be developed which provides enhanced security for images. This system uses two important techniques which is based on chaos theory namely: Confusion and Diffusion. Confusion part uses block scrambling and modified zigzag transformation while the Diffusion part uses 3D logistic map and key generation followed by additive cipher. This system also protects from statistical and differential attacks. The experimental results such as Entropy, Histogram analysis, Mean Absolute Error, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) proves that the security of the images has been preserved at a higher level and also prevents the unauthorized access to the sensitive information.

Abstract: Securing information became essential to exchange multimedia information safely. The exchanged data need to be transformed in a well-managed, secure, and reliable manner. In this paper, we will focus on securing RGB images via cryptography during transmission among users using our effective proposal of utilizing appropriate Pseudo Random Number Generator (PRNG). We implement many techniques of PRNG involved in two consecutive crypto-processes of substitution and transposition to present secure image transformation. Our technique proposal of PRNGs selection is based on testing to encrypt RGB images to be compared with current related used approaches. The work experimentation aims to identify suitability and reliability through security measures standard parameters. The research justifies its proper PRNG selection to model our approach as attractive effective work worth remarking.

Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

Abstract: We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html.

Abstract: The physical contrast of simple images such as sinusoidal gratings or a single patch of light on a uniform background is well defined and agrees with the perceived contrast, but this is not so for complex images. Most definitions assign a single contrast value to the whole image, but perceived contrast may vary greatly across the image. Human contrast sensitivity is a function of spatial frequency; therefore the spatial frequency content of an image should be considered in the definition of contrast. In this paper a definition of local band-limited contrast in images is proposed that assigns a contrast value to every point in the image as a function of the spatial frequency band. For each frequency band, the contrast is defined as the ratio of the bandpass-filtered image at the frequency to the low-pass image filtered to an octave below the same frequency (local luminance mean). This definition raises important implications regarding the perception of contrast in complex images and is helpful in understanding the effects of image-processing algorithms on the perceived contrast. A pyramidal image-contrast structure based on this definition is useful in simulating nonlinear, threshold characteristics of spatial vision in both normal observers and the visually impaired.

Abstract: To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.

Abstract: Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.