Comparison of Deep Learning and Conventional Demosaicing Algorithms for Mastcam Images

A Brief Review of Some Interesting Mars Rover Image Enhancement Projects

The Curiosity rover has landed on Mars since 2012. One of the instruments onboard the rover is a pair of multispectral cameras known as Mastcams, which act as eyes of the rover. In this paper, we summarize our recent studies on some interesting image processing projects for Mastcams. In particular, we will address perceptually lossless compression of Mastcam images, debayering and resolution enhancement of Mastcam images, high resolution stereo and disparity map generation using fused Mastcam images, and improved performance of anomaly detection and pixel clustering using combined left and right Mastcam images. The main goal of this review paper is to raise public awareness about these interesting Mastcam projects and also stimulate interests in the research community to further develop new algorithms for those applications.

A Review on Lossless Compression Techniques for Wireless Capsule Endoscopic Data

BACKGROUND

The videos produced during wireless capsule endoscopy have larger data size causing difficulty in transmission with limited bandwidth. The constraint on wireless capsule endoscopy hinders the performance of the compression module.

OBJECTIVES

The objectives of this paper are as follows: (i) to conduct an extensive review of the lossless compression techniques and (ii) to find out the limitations of the existing system and the possibilities for improvement.

METHODS

The literature review was conducted with a focus on the compression schemes satisfying minimum computational complexity, less power dissipation and low memory requirements for hardware implementation. A thorough study of various lossless compression techniques was conducted under two perspectives, i.e., techniques applied to Bayer CFA and RGB images. The detail of the various stages of wireless capsule endoscopy compression was investigated to have a better understanding. The suitable performance metrics for evaluating the compression techniques were listed from various literature studies.

RESULTS

In addition to the Gastrolab database, WEO clinical endoscopy atlas and Gastrointestinal atlas were found to be better alternatives for experimentation. Pre-processing operations, especially new subsampling patterns need to be given more focus to exploit the redundancies in the images. Investigations showed that encoder module can be modified to bring more improvement towards compression. The real-time endoscopy still exists as a promising area for exploration.

CONCLUSION

This review presents a research update on the details of wireless capsule endoscopy compression together with the findings as an eye-opener and guidance for further research.

Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels

In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obtain large numbers of proxy labels which denote the quality score of authentically distorted images by using a traditional no-reference IQA method. Then the deep network is trained by the proxy labels, in order to learn IQA-related knowledge from the amounts of images with their scores. Ultimately, we use fine-tuning to inherit knowledge represented in the trained network. During the procedure, the mapping relationship fits in with human visual perception closer. The experimental results demonstrate that the proposed algorithm shows an outstanding performance as compared with the existing algorithms. On the LIVE In the Wild Image Quality Challenge database and KonIQ-10k database (two standard databases for authentically distorted image quality assessment), the algorithm realized good consistency between human visual perception and the predicted quality score of authentically distorted images.

The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms

Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems.