Navaneeth Kamballur Kottayil

TL;DR: In this paper, a deep convolutional neural network based model DeepInSAR is proposed to solve both phase filtering and coherence estimation problems in InSAR phase computation, and a teacher-student framework is introduced to handle the issue of missing clean ground truth.

TL;DR: In this paper, the authors proposed a new convolutional neural network based model for no reference image quality assessment (NR-IQA) on HDR data, which predicts the amount and location of noise, perceptual influence of image pixels on the noise, and the perceived quality, of a distorted image without any reference image.

TL;DR: Quantitative and qualitative comparisons show that DeepInSAR achieves comparable or even better results than its stacked-based teacher method on new test datasets but requiring fewer pairs of SLCs as well as outperforms three other established non-stack based methods with less running time and no human supervision.

TL;DR: The proposed NR-IQA model is able to detect visual artifacts, taking into consideration perceptual masking effects, in a distorted HDR image without any reference, and predict HDR image quality as accurately as state-of-the-art full-reference IQA methods.

TL;DR: Zhang et al. as discussed by the authors proposed a low-level feature-based IQA technique, which applies filter-bank decomposition and center-surround methodology to extract and enhance perceptually significant features.