Underwater Image Restoration Using Fusion and Wavelet Transform Strategy

Rashid Khan
2015 Journal of Computers  
This paper describes a novel strategy to restore underwater images using fusion and wavelet Transform. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant
more » ... jects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework with wavelet transform also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images are characterized by reduced noise level, better exposedness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications. most of the existing techniques, our algorithm does not use supplemental information (e.g. images, depth estimation of the scene, hardware, etc.) processing only the content of the input degraded image. Our strategy is built on the image fusion principles. The proposed restoration algorithm employs three inputs that are mainly computed from the white balanced and min-max enhanced versions of the input image. Related Work Polarization analysis [1]: An algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range. A gray scale image, algorithm [2]: Proposed and compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images and gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. The method [3]: Employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by special maps in order to yield accurate haze free results. The method computes in a per-pixel fashion being straightforward to be implemented. A method [4]: For salient region detection that outputs full resolution saliency maps with well-defined boundaries of salient objects. These boundaries are preserved by retaining substantially more frequency content from the original image than other existing techniques. Our method exploits features of color and luminance, is simple to implement, and is computationally efficient. P. Burt and T. Adelson [5] : Describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a low pass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding. Proposed Wavelet Cum Fusion Strategy and Architecture Design In this paper we introduce a novel technique to restore underwater images. Different than most of the existing techniques, our algorithm does not use supplemental information (e.g. images, depth estimation of the scene, hardware, etc) processing only the content of the input degraded image. Our strategy is built on the image fusion principles. The proposed restoration algorithm employs three inputs that are mainly Journal of Computers 238
doi:10.17706/jcp.10.4.237-244 fatcat:gjd5ykpbnbhvdjboufjo63ekym