Type Of Image Degradation Research Articles

Optimization of Underwater Images Based on Gray World Algorithm and Jaffe-McGlamery Models

In this paper, a comprehensive scheme for underwater image processing is proposed based on the grayscale world algorithm and the Jaffe-McGlamery model. Firstly, a color bias detection based on grayscale world theory, a low light detection based on HSV color space, and a fuzzy detection method based on frequency domain and Laplace operator are designed to classify different types of image degradation. Subsequently, the corresponding scene degradation models are constructed for different degradation types through the simplified Jaffe-McGlamery model, and the image features under different water conditions are analyzed. Next, an improved gray world algorithm is used to eliminate color bias, the limiting contrast adaptive histogram equalization (CLAHE) technique is utilized to improve the image quality under low-light conditions, and the dark-channel a priori algorithm is optimized by the depth estimation and parameter adaptation modules to remove blurring. The proposed method in this study significantly improves the image quality in different scenarios and provides a new idea for underwater image processing.

PerNet: Progressive and Efficient All-in-One Image-Restoration Lightweight Network

The existing image-restoration methods are only effective for specific degradation tasks, but the type of image degradation in practical applications is unknown, and mismatch between the model and the actual degradation will lead to performance decline. Attention mechanisms play an important role in image-restoration tasks; however, it is difficult for existing attention mechanisms to effectively utilize the continuous correlation information of image noise. In order to solve these problems, we propose a Progressive and Efficient All-in-one Image Restoration Lightweight Network (PerNet). The network consists of a Plug-and-Play Efficient Local Attention Module (PPELAM). The PPELAM is composed of multiple Efficient Local Attention Units (ELAUs) and PPELAM can effectively use the global information and horizontal and vertical correlation of image degradation features in space, so as to reduce information loss and have a small number of parameters. PerNet is able to learn the degradation properties of images very well, which allows us to reach an advanced level in image-restoration tasks. Experiments show that PerNet has excellent results for typical restoration tasks (image deraining, image dehazing, image desnowing and underwater image enhancement), and the excellent performance of ELAU combined with Transformer in the ablation experiment chapter further proves the high efficiency of ELAU.

Analysis of digital noise and reduction methods on classifiers used in automated visual evaluation in cervical cancer screening.

The burden of cervical cancer disproportionately falls on low- and middle-income countries (LMICs). Automated visual evaluation (AVE) is a technology being considered as an adjunct tool for the management of HPV-positive women. AVE involves analysis of a white light illuminated cervical image using machine learning classifiers. It is of importance to analyze various impacts of different kinds of image degradations on AVE. In this paper, we report our work regarding the impact of one type of image degradation, Gaussian noise, and one of its remedies we have been exploring. The images, originated from the Natural History Study (NHS) and ASCUS-LSIL Triage Study (ALTS), were modified by the addition of white Gaussian noise at different levels. The AVE pipeline used in the experiments consists of two deep learning components: a cervix locator which uses RetinaNet (an object detection network), and a binary pathology classifier that uses the ResNeSt network. Our findings indicate that Gaussian noise, which frequently appears in low light conditions, is a key factor in degrading the AVE's performance. A blind image denoising technique which uses Variational Denoising Network (VDNet) was tested on a set of 345 digitized cervigram images (115 positives) and evaluated both visually and quantitatively. AVE performances on both the synthetically generated noisy images and the corresponding denoised images were examined and compared. In addition, the denoising technique was evaluated on several real noisy cervix images captured by a camera-based imaging device used for AVE that have no histology confirmation. The comparison between the AVE performances on images with and without denoising shows that denoising can be effective at mitigating classification performance degradation.

Enhanced Attention-Based Back Projection Network for Image Super-Resolution in Sensor Network

In this article, we propose a novel method for image restoration in sensor network. Deep learning based image Super-Resolution (SR) is such process which takes low-resolution image as input and outputs high-resolution one by an end-to-end trainable architecture. It has shown rapid development in this field by constantly deepening and widening the SR model or combining with other specially designed structures. Recently non-local attention mechanism is proposed and introduced into SR field which focuses on the self-similarity of single feature or difference between channels, but those methods don’t use the mechanism to explore the relationship between features in different layers. In this article, we design a Back Projection Attention Module to learn the cross-relation between different types of image degradation and high-resolution components. Then we construct our network by stacking the iterative up-sampling and down-sampling process and concatenating all outputs of up-sampling process to finally improve the ability of characteristic expression. Extend experiments on four commonly used datasets show the proposed model can improve the performance in quantitative and qualitative measurements.

Measurement of image degradation: a no-reference approach

In modern communication systems, transmission of data, particularly that of images is very important. Unfortunately, the process is prone to different kinds of degradations. For instance, the large traffic on the network makes the reduction of data volume and storage imperative. This is achieved through compression techniques such as JPEG and JPEG2000, at the expense of image quality, unfortunately. Image quality also deteriorates on account of the state of the transmission channel and the amount of losses occurring. Another common type of image degradation is blurring. At the receiving end, identifying the degradation is an important requirement for applications like blind image denoising so that appropriate techniques for removal may be adopted. Since the undegraded original image is usually not available, no-reference methods are not only attractive but also essential. This article proposes a novel no-reference method which can clearly distinguish between noise, compression and blurring. This is achieved in two stages – the first distinguishes between noise and compression/blurring while the second distinguishes between compression and blurring. Finally, a quantification of the degradation suffered is also carried out, which forms another important contribution of the approach. The primary tool employed is the Discrete Wavelet Transform (DWT) and the differential behaviour of the image DWT coefficients to noise, compression and blurring, at various levels of resolution. The use of the properties of the inter-level coefficients make this approach unique in nature. These properties have been explored and analyzed in detail. A large number of simulations using images from popular databases, establish the usefulness of the approach.

Image Restoration Model using Total Variance, Bilateral and Wavelet Denoising Filter

iNoise in images are most common due to various degradation. Noises in images are random variations in images due to lighting conditions, camera electronics, surface reflectance, lens, atmospheric conditions and motions (Either camera is moving or object is moving). Image Restoration is a process which restores a degraded image into its original image which has been degraded by some degradation model which degraded the image. Images are degraded due to various reasons. The first and foremost reason for image degradation is the fault in the imaging devices during the image acquisition process. The noise is generated in the imaging devices and is propagated to the image. The second source of degradation in image is the noise added during the image transmission. This type of image degradation is most common. The third source of image degradation is due to the motion blur and atmospheric turbulence. This paper analyzes various image noise models and restoration techniques. Particularly in analyses three kind of filters namely total variance filter, bilateral filter and wavelet image denoising. The image restoration is measured using the PSNR and SSI of original and degraded images

Local binary features for texture classification: Taxonomy and experimental study

Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected.In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.

Performance and sensitivity evaluation of 3D spot detection methods in confocal microscopy.

Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive.

Adaptive centroid-finding algorithm for freeform surface measurements

Wavefront sensing systems measure the slope or curvature of a surface by calculating the centroid displacement of two focal spot images. Accurately finding the centroid of each focal spot determines the measurement results. This paper studied several widely used centroid-finding techniques and observed that thresholding is the most critical factor affecting the centroid-finding accuracy. Since the focal spot image of a freeform surface usually suffers from various types of image degradation, it is difficult and sometimes impossible to set a best threshold value for the whole image. We propose an adaptive centroid-finding algorithm to tackle this problem and have experimentally proven its effectiveness in measuring freeform surfaces.

Monkeys show recognition without priming in a classification task

Humans show visual perceptual priming by identifying degraded images faster and more accurately if they have seen the original images, while simultaneously failing to recognize the same images. Such priming is commonly thought, with little evidence, to be widely distributed phylogenetically. Following Brodbeck (1997), we trained rhesus monkeys (Macaca mulatta) to categorize photographs according to content (e.g., birds, fish, flowers, people). In probe trials, we tested whether monkeys were faster or more accurate at categorizing degraded versions of previously seen images (primed) than degraded versions of novel images (unprimed). Monkeys categorized reliably, but showed no benefit from having previously seen the images. This finding was robust across manipulations of image quality (color, grayscale, line drawings), type of image degradation (occlusion, blurring), levels of processing, and number of repetitions of the prime. By contrast, in probe matching-to-sample trials, monkeys recognized the primes, demonstrating that they remembered the primes and could discriminate them from other images in the same category under the conditions used to test for priming. Two experiments that replicated Brodbeck's (1997) procedures also produced no evidence of priming. This inability to find priming in monkeys under perceptual conditions sufficient for recognition presents a puzzle.

Visual perception of vibration-distorted thermal images

The target recognition ability of the human visual system can be severely affected by vibration of the imaging system. Although vibration can be minimized by proper design, it is often the main limitation on visual perception despite the best attempts at stabilization. There are two types of image degradation caused by vibration of the camera. The first is the vibration of the line of sight (LOS), causing location changes of the scene in successive frames. The second degradation type is the blur induced in each frame of the sequence due to motion during the exposure. We investigate the relative effect of these two degradation sources on the human visual system recognition abilities by conducting a series of psychophysical experiments. These experiments show that object recognition and orientation recognition abilities may be affected more by the motion blur of each frame than by the oscillation of the scene. An important implication of these results on digital image sequence restoration algorithms is that under certain conditions, the main effort should be put toward motion deblurring rather than on the precise registration of the frames.

Method of predicting the probability of human observers recognizing targets in simulated thermal images

A series of psychophysical trials is conducted to study the human perception of images of various shapes that are degraded by various combinations of Gaussian blurring, sampling, and fixed pattern noise. These images were created using computer programs designed to simulate the types of image degradation associated with thermal im- agers that employ focal plane arrays (FPAs) of detectors. A total of 46 observers participated in these trials, during which over 130,000 obser- vations were recorded. The responses collected during the trials are used to develop an empirical model of human vision applicable to 2-D images having a gray scale, but not including other colors. This model can be used to predict the probability of a human observer correctly distinguishing between images of two different objects. © 1998 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(98)00303-1)