Study on Algorithms for Image Super-resolution based on Filtering and Learning Methods

Abstraksi

This thesis focuses on developing theory and algorithms for the single-image


super-resolution problem based on filtering and learning methods. Our pro-

posed methods are divided into three categories.


First part, First-order Derivatives- based Super-resolution is filtering based

method. A single fast super-resolution method based on first-order derivatives

from neighbor pixels is proposed. The basic idea of the proposed method is

to exploit a first-order derivatives component of six edge directions around a

missing pixel; followed by back projection to reduce noise estimated by the


difference between simulated and observed images. Using first-order deriva-

tives as a feature, the proposed method is expected to have low computational


complexity, and it can theoretically reduce blur, blocking, and ringing artifacts


in edge areas compared to previous methods. Experiments were conducted us-

ing 900 natural grayscale images from the USC-SIPI Database. We evaluated


the proposed and previous methods using peak signal-to-noise ratio, structural


similarity, feature similarity, and computation time. Experimental results indi-

cate that the proposed method clearly outperforms other state-of-the-art algo-

rithms such as fast curvature based interpolation.


Second part, Super-Resolution via Adaptive Multiple Sparse Representation

is learning based method. We propose a super-resolution algorithm based on

adaptive sparse representation via multiple dictionaries for images taken by

Unmanned Aerial Vehicles (UAVs). The super-resolution attainable through

the proposed algorithm can increase the precision of 3D reconstruction from


UAV images, enabling the production of high-resolution images for construct-

ing high-frequency time series and for high-precision digital mapping in agri-

culture. The basic idea of the proposed method is to use a field server or


ground-based camera to take training images and then construct multiple pairs

of dictionaries based on selective sparse representations to reduce instability

during the sparse coding process. The dictionaries are classified on the basis

of the edge orientation into five clusters: 0, 45, 90, 135, and non-direction.


The proposed method is expected to reduce blurring, blocking, and ringing

artifacts especially in edge areas. We evaluated the proposed and previous


methods using peak signal-to-noise ratio, structural similarity, feature similar-

ity, and computation time. Our experimental results indicate that the proposed


method clearly outperforms other state-of-the-art algorithms based on qualita-

tive and quantitative analysis. In the end, we demonstrate the effectiveness of


our proposed method to increase the precision of 3D reconstruction from UAV

images.

Last part, Deep Residual Learning Super-resolution is learning based method.

The light and efficient residual network for super-resolution is proposed. We


adopt inception module from GoogLeNet to exploit the features from the low-

resolution images and residual learning to have fast training steps. The pro-

posed network called Deep Residual Learning Super-resolution (DRLSR). The


network is proven to have fast convergence and low computational time. It is

divided into three parts: feature extraction, mapping, and reconstruction. In


the feature extraction, we apply inception module followed by dimensional re-

duction. Then, we map the features using simple convolutional layer. Finally,


we reconstruct the HR component using inception module and 1⇥1 convolu-

tional layer. The experimental results show our proposed method can reduce


more than half of computational time from the-state-of-the-art methods, while

still having clean and sharp images.

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