https://researchportal.port.ac.uk/portal/files/7059144/1_s2.0_S0925231217305246_main.pdf

Underwater image enhancement via extended multi-scale Retinex

https://link.springer.com/content/pdf/10.1057%2Fjors.1977.206.pdf

Multiple Hypergeometric Functions and Applications

Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images have low contrast, blurred details, and color distortion. These characteristics can significantly interfere with visual tasks, such as segmentation and tracking. This paper proposes an underwater image enhancement solution through a deep residual framework. First, the cycle-consistent adversarial networks (CycleGAN) is employed to generate synthetic underwater images as training data for convolution neural network models. Second, the very-deep super-resolution reconstruction model (VDSR) is introduced to underwater resolution applications; with it, the Underwater Resnet model is proposed, which is a residual learning model for underwater image enhancement tasks. Furthermore, the loss function and training mode are improved. A multi-term loss function is formed with mean squared error loss and a proposed edge difference loss. An asynchronous training mode is also proposed to improve the performance of the multi-term loss function. Finally, the impact of batch normalization is discussed. According to the underwater image enhancement experiments and a comparative analysis, the color correction and detail enhancement performance of the proposed methods are superior to that of previous deep learning models and traditional methods.

/pdf/underwater-image-enhancement-with-a-deep-residual-framework-1x60mpue9e.pdf

Underwater Image Enhancement With a Deep Residual Framework

Emerging cellular technologies such as those proposed for use in 5G communications will accommodate a wide range of usage scenarios with diverse link requirements. This will necessitate operation over a versatile set of wireless channels ranging from indoor to outdoor, from line-of-sight (LOS) to non-LOS, and from circularly symmetric scattering to environments which promote the clustering of scattered multipath waves. Unfortunately, many of the conventional fading models lack the flexibility to account for such disparate signal propagation mechanisms. To bridge the gap between theory and practical channels, we consider $\kappa$ – $\mu$ shadowed fading, which contains as special cases the majority of the linear fading models proposed in the open literature. In particular, we propose an analytic framework to evaluate the average of an arbitrary function of the signal-to-noise-plus-interference ratio (SINR) over $\kappa$ – $\mu$ shadowed fading channels by using an orthogonal expansion with tools from stochastic geometry. Using the proposed method, we evaluate the spectral efficiency, moments of the SINR, and outage probability of a $K$ -tier heterogeneous cellular network with $K$ classes of base stations (BSs), differing in terms of the transmit power, BS density, shadowing, and fading characteristics. Building upon these results, we provide important new insights into the network performance of these emerging wireless applications while considering a diverse range of fading conditions and link qualities.

A Comprehensive Analysis of 5G Heterogeneous Cellular Systems Operating Over $\kappa$ – $\mu$ Shadowed Fading Channels

2 Abstract: Image enhancement is a processing on an image in order to make it more appropriate for certain applications. It is used to improve the visual effects and the clarity of image or to make the original image more conducive for computer to process. Contrast enhancement changing the pixels intensity of the input image to utilize maximum possible bins. We need to study and review the different image contrast enhancement techniques because contrast losses the brightness in enhancement of image. By considering this fact, the mixture of global and local contrast enhancement techniques may enhance the contrast of image with preserving its brightness. There are many image contrast enhancement techniques such as HE, BBHE, DSIHE, MMBEBHE, RMSHE, MHE. BPDHE, RSWHE, GHE, LHE and LGCS. This paper focuses on the comparative study of contrast enhancement techniques with special reference to local and global enhancement techniques. Also proposed solution is identified to apply to this enhancement technique. This novel method will use in many fields, such as medical image analysis, remote sensing, HDTV, hyper spectral image processing, industrial X-ray image processing, microscopic imaging etc.

http://ijirset.com/upload/july/28_REVIEW.pdf

Review of various image contrast enhancement techniques

The dynamic range of a camera is much lesser than that of human visual system. This causes images taken by the camera to look different from how the scene would have looked to a naked eye. Multi Scale Retinex with Color Restoration (MSRCR) algorithm enhances images taken under a wide range of nonlinear illumination conditions to the level that a user would have perceived it in real time. But there are parameters used in this enhancement method that are image dependent and have to be varied based on the images under consideration. In this paper we propose a completely automated approach for MSRCR by obtaining parameter values from the image being enhanced.

An automated multi Scale Retinex with Color Restoration for image enhancement

The downlink coverage probability of a cellular network, when the base station locations are modeled by a Poisson point process, is known when the desired channel is Nakagami distributed with an integer shape parameter. However, for many interesting fading distributions such as Rician, Rician shadowing, $ {\kappa {-}\mu }$ , ${\eta {-}\mu }$ , etc., the coverage probability is unknown. ${\kappa {-}\mu }$ shadowed fading is a generic fading distribution whose special cases are many of these popular distributions known so far. In this letter, we derive the coverage probability when the desired channel experiences ${\kappa {-}\mu }$ shadowed fading. Using numerical simulations, we verify our analytical expressions.

Coverage Analysis in Downlink Poisson Cellular Network With $\kappa $ – $\mu $ Shadowed Fading

The downlink coverage probability of a cellular network, when the base station locations are modelled by a Poisson point process (PPP), is known when the desired channel is Nakagami distributed with an integer shape parameter However, for many interesting fading distributions such as Rician, Rician shadowing, $\kappa$-$\mu$, $\eta$-$\mu$, etc, the coverage probability is unknown $\kappa$-$\mu$ shadowed fading is a generic fading distribution whose special cases are many of these popular distributions known so far In this letter, we derive the coverage probability when the desired channel experiences $\kappa$-$\mu$ shadowed fading Using numerical simulations, we verify our analytical expressions

Coverage Analysis In Downlink Poisson Cellular Network With $\kappa$-$\mu$ Shadowed Fading

One of the most interesting problems in recent times in image processing and computer vision is fog, haze and rain removal from images. In this paper we shall consider the problem of haze removal. One of the latest haze removal algorithms proposed by Kaiming et al. [1] uses a dark channel prior based approach for haze removal. Though this approach gives very good results, this method is computationally complex. In this paper we propose a RETINEX based approach that gives good results and is also computationally simpler.

A RETINEX based haze removal method

Image fusion is a technique of combining two or more images so that the combined image is better enhanced than all these images. We propose that a fusion based approach on Multi Scale Retinex with Color Restoration(MSRCR) would give better image enhancement. Lower dynamic range of a camera as compared to human visual system causes images taken to be extremely dependent on illuminant conditions. MSRCR algorithm enhances images taken under a wide range of nonlinear illumination conditions to the level that a user would have perceived it in real time.

Sudharsan Parthasarathy

Papers

An automated multi Scale Retinex with Color Restoration for image enhancement

Coverage Analysis in Downlink Poisson Cellular Network With $\kappa $ – $\mu $ Shadowed Fading

Coverage Analysis In Downlink Poisson Cellular Network With $\kappa$-$\mu$ Shadowed Fading

A RETINEX based haze removal method

Fusion based Multi Scale RETINEX with Color Restoration for image enhancement