Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization

TL;DR

This paper introduces a novel patch-wise restoration method for non-uniform aerial images, combining a non-local Retinex model with shearlet-based regularization to effectively remove space-varying blur and uneven illumination, resulting in high-quality image recovery.

Contribution

It develops a new non-local Retinex model for reflectance estimation and integrates improved shearlet transform and adaptive total generalized variation for enhanced regularization in aerial image restoration.

Findings

Effective removal of space-varying illumination and motion blur

Superior image detail recovery compared to state-of-the-art methods

High objective and subjective image quality results

Abstract

Aerial images are often degraded by space-varying motion blur and simultaneous uneven illumination. To recover high-quality aerial image from its non-uniform version, we propose a novel patch-wise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illuminated conditions. A non-local Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter the uneven illumination is corrected well. And then non-uniform coupled blurring in the enhanced reflectance image is alleviated and transformed towards uniform distribution, which will facilitate the subsequent deblurring. For constructing the multi-scale sparsified regularizer, the discrete shearlet transform is improved to better represent anisotropic image features in term of directional sensitivity and selectivity. In addition, a new adaptive variant of total generalized variation is proposed for the structure-preserving regularizer. These complementary regularizers are elegantly integrated into an objective function. The final deblurred image with uniform illumination can be extracted by applying the fast alternating direction scheme to solve the derived function. The experimental results demonstrate that our algorithm can not only remove both the space-varying illumination and motion blur in the aerial image effectively but also recover the abundant details of aerial scenes with top-level objective and subjective quality, and outperforms other state-of-the-art restoration methods.