Combining Iterative Inverse Filter with Shock Filter for Baggage Inspection Image Deblurring

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

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Stack-Based Scale-Recurrent Network for Image Deblurring

Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition ◽

10.1145/3436369.3437419 ◽

2020 ◽

Author(s):

Yanqiu Wu ◽

Chaoqun Hong ◽

Chaohui Tang ◽

Zhiqiang Zeng

Keyword(s):

Image Deblurring ◽

Recurrent Network

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A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽

10.3390/s21165312 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5312

Author(s):

Yanni Zhang ◽

Yiming Liu ◽

Qiang Li ◽

Jianzhong Wang ◽

Miao Qi ◽

...

Keyword(s):

Deep Learning ◽

Image Deblurring ◽

Image Features ◽

Image Feature ◽

Model Complexity ◽

Small Scale ◽

Feature Maps ◽

Learning Framework ◽

Channel Information ◽

Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

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