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Fish species classification in underwater images is an emerging research area for scientists and researchers in the field of image processing. Fish species classification in underwater images is an important task for fish survey i.e. to audit ecological balance, monitoring fish population and preserving endangered species. But the phenomenon of light scattering and absorption in ocean water leads to hazy, dull and low contrast images making fish classification a tedious and tough task. Convolutional Neural Networks (CNNs) can be the solution for fish species classification problem but the scarcity of ample fish images leads to the serious issue of training a neural network from scratch. To overcome the issue of limited dataset the present paper proposes a transfer learning based fish species classification method for underwater images. ResNet-50 network has been used for transfer learning as it reduces the vanishing gradient problem to minimum by using residual blocks and thus improving the accuracies. Training only last few layers of ResNet-50 network with transfer learning increases the classification accuracy despite of scarce dataset. The proposed method has been tested on two datasets comprising of 27, 370 (i.e. large dataset) and 600 images (i.e. small dataset) without any data augmentation. Experimental results depict that the proposed network achieves a validation accuracy of 98.44% for large dataset and 84.92% for smaller dataset. With the performance analysis, it is observed that this transfer learning based approach led to better results by providing high precision, recall and F1score values of 0.94, 0.85 and 0.89, respectively.

Underwater image enhancement has been attracting researchers in present day scenario for exploring the marine life. But underwater images suffer from various glitches like haziness, low contrast and faded colours due to absorption and scattering properties of light in water. To overcome these issues, the present paper proposes an enhancement method for underwater images. The proposed enhancement method comprises of three steps, i.e. automatic white balancing, dehazing and Rayleigh stretching in spatial domain. Automatic white balancing technique removes the colour cast from underwater images. Haze removal algorithm efficiently overcomes the haziness but it also reduces the local contrast and making the image appear dull. For improving the dehazing result and the visual quality simultaneously, the proposed method process the image by histogram stretching technique based on Rayleigh distribution. The proposed method corrects the colour cast and improves the contrast along with removing the haze from underwater images. Subjective and objective analysis on U 45 dataset validates the efficiency of proposed method and the enhanced images achieve better visual quality as compared to the existing methods. High values of EMEE, EME, UIQM and UCIQE for different types of underwater images further proves the potential and efficacy of the proposed method. INTRODUCTIONAn unexplored huge world present under oceans is attracting various researcher and scientists to study and investigate variety of marine life forms, incredible landscape, mysterious shipwrecks and beautiful coral reefs. Underwater environment is also gaining substantial attention due to increase in optical robotic applications and marine engineering [1,2]. But complex underwater environment and poor lighting conditions are major challenges to capture good quality underwater images. Water is a denser medium than the air and sunlight is reflected and refracted partially as it enters the ocean due to transition from one medium to the other. Less light will be reflected when ocean water is calm and smooth rather than when it is turbulent. The part of light which penetrates the ocean surface is affected by the phenomenon of refraction because light travels faster in air as compared to water. These refracted rays of light further interact with the water molecules and suspended solid particles leading to scattering and absorption. Scattering reduces intensity of light causing loss in contrast and haziness in underwater images. Whereas, absorption of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Abstract-For energy harvesting applications a new design of a coplanar waveguide (CPW) fed monopole antenna is presented. It covers almost all useful band ranges from 900 MHz-9.9 GHz (Radio, GSM, ISM, UWB bands). It also provides band reject characteristics for the range 3.1 GHz-5.6 GHz (HIPERLAN, C-Band, and W-LAN) to avoid interference from this range. The new design is based on the modification of coplanar waveguide (CPW) structure and optimizing the gap between patch and CPW ground for covering the ultra wideband (UWB) range and other useful ranges (Radio, GSM and ISM). Bandwidth enhancement and impedance matching for UWB range have been obtained by chamfering the corners, cutting two slots in CPW ground and dual stubs. The new design incorporates a parasitic patch above the antenna patch for tunning the desired band rejection. The entire design has been optimized at various stages during its evolution. The structure is compact in size 50×40×1.6 mm 3 . It may also be used for mobile, military and satellite applications.