Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Abstract

ABSTRACT:Marine gas seeps, such as in the Panarea area near Sicily (McGinnis et al., 2011), emit large amounts of methane and carbon-dioxide, greenhouse gases. Better understanding their impact on the climate and the marine environment requires precise measurements of the gas flux. Camera based bubble measurement systems suffer from defocus blur caused by a combination of small depth of field, insufficient lighting and from motion blur due to rapid bubble movement. These adverse conditions are typical for open sea underwater bubble images. As a consequence so called 'bubble boxes' have been built, which use elaborate setups, specialized cameras and high power illumination. A typical value of light power used is 1000W (Leifer et al., 2003). In this paper we propose the compensation of defocus and motion blur in underwater images by using blind deconvolution techniques. The quality of the images can be greatly improved, which will relax requirements on bubble boxes, reduce their energy consumption and widen their usability.

The Bubble Box: Towards an Automated Visual Sensor for 3D Analysis and Characterization of Marine Gas Release Sites

“…The results of deconvolution indicate that the fattening of bubble contours caused by motion and defocus blur, which leads to an overestimation of the bubble size, can be reduced. This has also been confirmed in a detailed analysis in [ 38 ]. Blind deconvolution is computationally expensive and the run-time for the gradient sparsity algorithm in a Matlab implementation is up to on an image with 261 pixels width and 612 pixels height.…”

“…For motion-blurred images, the deblurring of [ 41 ] also shows a good improvement in sharpness, but remains incomplete, see Figure 12 b. Clearer contours can be achieved as described in [ 38 ], i.e. , by using times the weight for the sparsity prior of the blur kernel (compare Figure 12 b,c).…”

“…This will compensate for the influence of motion and defocus blur to some extent. This section focuses on the practical application of the algorithm, for more details, see results already published in [ 38 ].…”