Denoising, Edge Aware Restoration and Enhancement of Single Shallow Coastal Water Image

The dissolved particles and their resultant scattering are the underlying cause of low contrast and blur thereby producing poor quality underwater images. Single-shot shallow coastal underwater images are much in need of the preprocessing steps viz. image enhancement and restoration. The underwater image processing operations like classification, object detection and computer vision require an enhancement/restoration preprocessing. The paper aims to restore the visibility of objects in turbid water images with its effective edge aware restoration cum enhancement model. The restricted rolling guidance filter on the DCP-based restoration method produces better edge aware restoration and denoised output image. The model-based (MB) dark channel prior (DCP) along with an edge emphasis and contrast enhancement achieves the essential dehazing and improvement in contrast for the heavily blurred underwater images. The subjective and objective projections are an evidence for the same. This edge preserving and denoising nature of the model is also exhibited with comparisons made with promising algorithms over the decade.

Learning-Based Dark and Blurred Underwater Image Restoration

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.

Underwater Image Processing and Object Detection Based on Deep CNN Method

Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a 1∗1 convolution kernel is used on the 26∗26 feature map, and then a downsampling layer is added to resize the output to equal 13∗13. In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation.

Restoration and Enhancement of Underwater Under-Exposure Images with Detail-Preserving

Underwater images have great practical value in many fields such as underwater archeology, seabed mining, and underwater exploration. Due to the complex underwater environment, there are problems such as poor light, low contrast, and color degradation. Traditional underwater image processing methods cannot well achieve the goal of clear display under extreme conditions. This paper proposes a method for restoration and enhancement of underwater under-exposure images that protects edge details and enhances image color. Firstly, the underwater image was preprocessed, denoising with improved wavelet threshold function, defogging with the Multi-Scale Retinex Color Restoration (MSRCR) and guided filter method. Then, the method of adaptive exposure graph is used to enhance the under-exposure image. Finally, the deep learning algorithm combined with the Non-Subsampled Contour Transform (NSCT) technology is used to solve the problem of color degradation and edge texture weakening. Experiments show that compared with other underwater image processing methods, this method greatly improves the clarity of the image, enhances the color saturation and the edge texture details of the image, and has a better visual effect.

Underwater Image Processing using Graphics Processing Unit (GPU)

Image processing is a method used for enhancement of an image or to extract some useful information from the image. It is a type of signal processing in which input is an image and output may be an image or any characteristics/features associated with that image. In this paper we will be focusing on a specific type of Image Processing i.e. Underwater Image Processing. Underwater Image Processing has always faced the problem of imbalance in colour distribution and this problem can be tackled by the simplest algorithm for colour balancing. We will be proceeding with the assumption that the highest values of R, G, B observed in the image corresponds to white and the lowest values corresponds to darkness. The underwater images are majorly saturated by blue colour because of its short wavelength and in this paper, we aim to enhance the image. We proposed a colour balancing algorithm for normalizing the image. The entire process will first be carried out on a CPU followed by a GPU. We will then compare the speedup obtained. Speedup is an important parameter in the field on image processing since a better speedup can help reduce the computation time significantly while maintaining a higher efficiency.

Enhancement of Underwater Images using Pixel Arrangements and Image Enhancement Algorithm

Underwater image processing is faced with a number of challenges from distinct resolutions, format variations, scattering, absorption of light etc. It is also affected by contrast difference and orientation. Conveying different resolved underwater images using enhanced pixel arrangement and image algorithm. This can be proceeded by converting the multi scale fused resolution images to high resolution images. Image enhancements has some in-built pixels properties having low intensities in at least one-color channel. Such kind of images are then converted to high resolution imaging by using the tools of enhanced pixel arrangement algorithm and then the output images are clarified.