Regularised digital-level corrections for infrared image correlation

Abstract

Global digital image correlation (DIC) applied to infrared images is considered for a test where very significant and localized laser heating is applied. Because of temperature changes and high gradients, digital levels are to be corrected in addition to the displacement registration. Digital level corrections and kinematics are decomposed over an unstructured mesh to account for steep gradients. A regularization strategy is used to mend ill-posedness penalizing rapid variation of corrections and displacements. High gradients motivate further softening of the regularization (i.e., reduction of the regularization length scale) based on prior estimates. The methodology is tested on different experimental cases in which DIC residuals are drastically reduced and systematic displacement errors are cut down to 10 −2 pixel after gray level corrections (performed on numerical cases). Additionally, the regularization improves the convergence speed as well as the quality of the sought fields. The capability of the code to account for blur corrections is illustrated on a practical case.

Spatiotemporal regularization for digital image correlation: Application to infrared camera frames

“…In the ROI, dedicated spatial regularization kernels are added to the DIC cost function. Such filters penalize displacement field fluctuations and GL correction fields . Regularization over the time coordinate can be applied by integrating over few frames or series of frames to filter out unwanted time fluctuations.…”

“…The space/time DIC approach follows the same general principles in the work of Besnard et al, 28 whereas the algorithm is implemented in the same framework of Tomičević et al 16 The use of unstructured T3 mesh support and GL conservation assumption relaxation allows us to handle images with GL variations. 19 In the ROI, dedicated spatial regularization kernels are added to the DIC cost function. Such filters penalize displacement field fluctuations 16 and GL correction fields.…”

“…Such filters penalize displacement field fluctuations 16 and GL correction fields. 19 Regularization over the time coordinate can be applied by integrating over few frames 29 or series of frames 28 to filter out unwanted time fluctuations. However, in this work, another route is preferred and uses a model-based representation, which is a model reduced using the Karhunen-Loève decomposition 32,33 (KLD) to regularize the time evolution.…”

“…() When, in addition to accounting for a displacement field, gray‐level (GL) corrections are necessary, say, to register infrared (IR) frames, a (even) more general approach can be implemented. () The same type of filtering can be applied to dampen out inherent spatial fluctuations …”

“…17,18 The same type of filtering can be applied to dampen out inherent spatial fluctuations. 19 As regularization is enhanced, the danger is to betray reality. Thus, one safe way to proceed is to incorporate a model that is as faithful as possible to the actual case.…”