Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.

Obtaining clear images of underwater scenes with descriptive details is an arduous task. Conventional imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition errors. Consequently, a need for a system that produces clear images for underwater image study has been necessitated. To overcome problems in resolution and to make better use of the Super-Resolution (SR) method, this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network (AlphaGAN) model, named Alpha Super Resolution Generative Adversarial Network (AlphaSRGAN). The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise details. Images undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the generator. After the images are processed by the generator network, they are passed through an adversarial method for training models. The dataset used in this paper to learn Single Image Super Resolution (SISR) is the USR 248 dataset. Training supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image quality. Appraisal of images is done with reference to factors like local style information, global content and color. The dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images-high (640 × 480) and low (80 × 60, 160 × 120, and 320 × 240). Paired instances of different sizes-2×, 4× and 8×-are also present in the dataset. Parameters like Mean Opinion Score (MOS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Underwater Image Quality Measure (UIQM) scores have been compared to validate the improved efficiency of our model when compared to existing works.

The process of augmenting the number of images in a dataset is called Image Augmentation. Data volume is essential to process and generate digital outputs from a variety of features. This work focuses on the image augmentation using a hybrid RANSAC algorithm. The features extracted is used to join or merge the images by the blending of images. The proposed RANSAC algorithm is used to extract features from four images and produce the desired mosaiced image. A mosaiced picture is best suited for aerial photos and real-world objects. The blur metric of the proposed method is 185.2587 and which is 2.86% higher than the feathering blending algorithms. The total number of images in the dataset is 2100. The number of images after augmentation is 6300 with average accuracy of 95.6%. The reported remarkable results are beneficial to all the stakeholders on image augmentation.

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