Phase unwrapping algorithm is difficult to apply in INSAR image for the local high-density noise phase attributed to significant blocky noise. To achieve its application in such case, the pixels of noise phase are first detected, and are set to 0 with the automatic mask technique. For the phase that has a blocky noise region, the iteration algorithm of phase filling based on Least-Squares is developed in this study by calculating the unwrapping coefficient k to rebuild the true phase. The algorithm is promoted by MPIPU's ability to fill in the missing phase; it can also significantly suppress the error transfer attributed to iteration filling in the non-mask phase. Some experiments are performed on simulated data. As revealed from the results, the proposed method exhibits robust performance of phase unwrapping on local noise phase.
The restoration of nonuniform distorted infrared (IR) images is crucial for human visual perception and subsequent application tasks. However, existing methods sometimes fail to yield visually natural decompositions and perform insufficiently in the preservation of meaningful structures while suppressing disturbing noise. A spatially adaptive hybrid ℓ1 − ℓ2 variational framework for the nonuniform intensity correction of IR images is proposed. Considering the piecewise constant characteristics of latent images, a weighted ℓ1-norm regularization method is developed to constrain the local affinity of neighborhood pixels according to their intensity and structural priors, thereby significantly preserving structures while smoothly flattening areas. Additionally, an ℓ2-norm guided local smoothness constraint is incorporated with an absolute scale term provided by coarse estimation to characterize the bias field component to restrict potential solutions and enforce the bias component to be textureless. Moreover, the proposed ℓ1 − ℓ2 model is efficiently solved by an alternating direction method of multipliers scheme. Extensive experiments on both synthesized images and two real-world IR datasets indicate that the performance of the proposed method is superior to that of five existing algorithms both visually and numerically.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.